What Is a Database?

essay on database system

A database is simply a structured and systematic way of storing information to be accessed, analyzed, transformed, updated and moved (to other databases). 

To begin understanding databases, consider an Excel notebook or Google sheet. Spreadsheets like these are a basic form of a table. Databases are almost exclusively organized in tables and those tables have rows and columns. So, think of a simple database as a collection of spreadsheets (or tables) joined together in a systematic way.

Database Definition

A database is a way for organizing information, so users can quickly navigate data, spot trends and perform other actions. Although databases may come in different formats, most are stored on computers for greater convenience.

Databases are stored on servers either on-premises at an organization’s office or off-premises at an organization’s data center (or even within their cloud infrastructure). Databases come in many formats in order to do different things with various types of data. 

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Why Do We Use Databases?

Computerized databases were first introduced to the world in the 1960s and have since become the foundation for products, analysis, business processes and more. Many of the services you use online every day (banking, social media, shopping, email) are all built on top of databases.

Today, databases are used for many reasons.

Databases Hold Data Efficiently

We use databases because they are an extremely efficient way of holding vast amounts of data and information. Databases around the world store everything from your credit card transactions to every click you make within one of your social media accounts. Given there are nearly eight billion people on the planet,  that’s a lot of data . 

Databases Allow Smooth Transactions

Databases allow access to various services which, in turn, allow you to access your accounts and perform transactions all across the internet. For example, your bank’s login page will ping a database to figure out if you’ve entered the right password and username. Your favorite online shop pings your credit card’s database to pull down the funds needed for you to buy that item you’ve been eyeing. 

Databases Update Information Quickly

Databases allow for easy information updates on a regular basis. Adding a video to your TikTok account, directly depositing your salary into your bank account or buying a plane ticket for your next vacation are all updates made to a database and displayed back to you almost instantaneously. 

Databases Simplify Data Analysis

Databases make research and data analysis much easier because they are highly structured storage areas of data and information. This means businesses and organizations can easily analyze databases once they know how a database is structured. Common structures (e.g. table formats, cell structures like date or currency fields) and common database querying languages (e.g.,  SQL ) make database analysis easy and efficient. 

What Is a Database Management System?

A database management system (DBMS) is a software package we use to create and manage databases. In other words, a DBMS makes it possible for users to actually interact with the database. In other words, the DBMS is the user interface (UI) that allows us to access, add, modify and delete content from the database. There are several types of database management systems, including relations, non-relational and hierarchical.

Evolution of Databases

Storing information is nothing new, but the rise of computers in the 1960s marked a shift toward more digital forms of databases. While working for GE, Charles Bachman created the Integrated Data Store, ushering in a new age of computerized databases. IBM soon followed suit with its Information Management System, a hierarchical database. 

In the 1970s, IBM’s Edgar F. Codd released a paper touting the benefits of relational databases, leading to IBM and the University of California, Berkeley releasing their own models. Relational databases became popular in the following years, with more businesses developing models and using Structured Query Language (SQL). Even though object-oriented databases became an alternative in the 1980s, relational databases remained the gold standard. 

The invention of the World Wide Web led to greater demand for databases in the 1990s. MySQL and NoSQL databases entered the scene, competing with the commercial databases developed by businesses. Object-oriented databases also began to replace relational databases in popularity.        

During the 2000s and 2010s, organizations began to collect larger volumes of data, and many turned to the scalability offered by NoSQL databases. Distributed databases provided another way to organize this proliferating data , storing it away in multiple locations.  

Types of Databases

There are many types of databases used today. Below are some of the more prominent ones.

1. Hierarchical Databases 

Hierarchical databases were the earliest form of databases. You can think of these databases like a simplified family tree. There’s a singular parent object (like a table) that has child objects (or tables) under it. A parent can have one or many child objects but a child object only has one parent. The benefit of these databases are that they’re incredibly fast and efficient plus there’s a clear, threaded relationship from one object to another. The downside to hierarchical databases is that they’re very rigid and highly structured. 

2. Relational Databases  

Relational databases are perhaps the most popular type of database. Relational databases are set up to connect their objects (like tables) to each other with keys. For example, there might be one table with user information (name, username, date of birth, customer number) and another table with purchase information (customer number, item purchased, price paid). In this example, the key that creates a relationship between the tables is the customer number. 

3. Non-Relational or NoSQL Databases  

Non-relational databases were invented more recently than relational databases and hierarchical databases in response to the growing complexity of web applications. Non-relational databases are any database that doesn’t use a relational model. You might also see them referred to as  NoSQL databases . Non-relational databases store data in different ways such as unstructured data, structured document format or as a graph. Relational databases are based on a rigid structure whereas non-relational databases are more flexible.

4. Cloud Databases

Cloud databases refer to information that’s accessible in a hybrid or cloud environment. All users need is an internet connection to reach their files and manipulate them like any other database. A convenience of cloud databases is that they don’t require extra hardware to create more storage space. Users can either build a cloud database themselves or pay for a service to get started.

5. Centralized Databases

Centralized databases are contained within a single computer or another physical system. Although users may access data through devices connected within a network, the database itself operates from one location. This approach may work best for larger companies or organizations that want to prioritize data security and efficiency.

6. Distributed Databases

Distributed databases run on more than one device. That can be as simple as operating several computers on the same site, or a network that connects to many devices. An advantage of this method is that if one computer goes down, the other computers and devices keep functioning.  

7. Object-Oriented Databases 

Object-oriented databases perceive data as objects and classes. Objects are specific data — like names and videos — while classes are groups of objects. Storing data as objects means users don’t have to distribute data across tables. This makes it easier to determine the relationships between variables and analyze the data. 

8. Graph Databases

Graph databases highlight the relationships between various data points. While users may have to do extra work to determine trends in other types of databases, graph databases store relationships right next to the data itself. Users can then immediately see how various data points are connected to each other.  

What Are the Components of a Database?

The components of a database vary slightly depending on whether the database is hierarchical, relational or non-relational. However, here’s a list of database components you might expect to be associated with any database.

The database schema is essentially the  design of the database . A schema is developed at the early conceptual stages of building a database. It’s also a valuable source of ongoing information for those wanting to understand the database’s design. 

Constraints and Rules

Databases use constraints to determine what types of tables can (and cannot) be stored and what types of data can live in the columns or rows of the database tables, for example. These constraints are important because they ensure data is structured, less corruptible by unsanctioned  data structures and that the database is regulated so users know what to expect. These constraints are also the reason why databases are considered rigid.

Metadata is essentially the data about the data. Each database or object has metadata, which the database software reads in order to understand what’s in the database. You can think of metadata as the database schema design and constraints combined together so a machine knows what kind of database it is and what actions can (or can’t) be performed within the database. 

Query Language

Each database can be queried. In this case, “queried” means people or services can access the database. That querying is done by way of a particular language or code snippet. The most common querying language is SQL (Structured Query Language) but there are also many other languages and even SQL variations like  MySQL , Presto and Hive.

Each database is a collection of objects. There are a few different types of objects stored within databases such as tables, views, indexes, sequences and synonyms. The most well known of these are tables, like spreadsheets, that store data in rows and columns. You may also hear the term “object instance,” which is simply an instance or element of an object. For example, a table called “Transactions” in a database is an instance of the object-type table.

Database Advantages

The structured nature of databases offers a range of benefits for professional and casual users alike. Below are some of the more prominent advantages:  

  • Improved data sharing and handling
  • Improved data storage capacity
  • Improved data integrity and data security
  • Reduced data inconsistency 
  • Quick data access
  • Increased productivity
  • Improved data-driven decision making  

Database Disadvantages

Although databases can be helpful for many, there are some limitations to consider before investing in a database: 

  • High complexity
  • Required dedicated database management staff
  • Risk of database failure​

Applications of Databases

When used correctly, databases can be a helpful tool for organizations in various industries looking to better arrange their information. Common use cases include:

  • Healthcare: storing massive amounts of patient data .
  • Logistics: monitoring and analyzing route information and delivery statuses.
  • Insurance: storing customer data like addresses, policy details and driver history.
  • Finance: handling account details, invoices, stock information and other assets.
  • E-commerce: compiling and arranging data on products and customer behavior.
  • Transportation: storing passengers’ names, scheduled flights and check-in status.
  • Manufacturing: keeping track of machinery status and production goals.
  • Marketing: collecting data on demographics, purchasing habits and website visits.
  • Education: tracking student grades, course schedules and more.
  • Human resources: organizing personnel info, benefits and tax information.

Future of Databases

As organizations handle increasing amounts of data, future databases must be able to keep up. Users will expect databases to be accessible across the globe and able to deal with limitless volumes of data. As a result, it’s likely that more companies will migrate their data to cloud environments. The percent of data stored in the cloud doubled between 2015 and 2022, and there’s reason to believe this percentage will only grow in the years to come. 

With the increase in data has also come a spike in cybersecurity threats , so organizations can be expected to complement their cloud environments with reinforced security measures . Databases will become more easily accessible only for authorized personnel while companies adopt tools and best practices for keeping their data out of the wrong hands.

Frequently Asked Questions

What is the difference between a database and a spreadsheet.

Spreadsheets organize data into rows and columns, with each individual cell housing the actual data. Databases also employ rows and columns, but each cell contains a record of data gathered from an external table. As a result, databases provide more ways to arrange and structure information as opposed to spreadsheets.

What is the most commonly used database type?

The most commonly used database type is the relational database.

What is the definition of a database?

A database is highly organized information that is designed to be easily accessible and navigable for users. Most databases are stored on computers, making it possible to quickly analyze, transform and manipulate data in other ways.

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Handbook on Data Management in Information Systems pp 221–283 Cite as

Advanced Database Systems

  • Gottfried Vossen 6 , 7  

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Part of the book series: International Handbooks on Information Systems ((INFOSYS))

Database systems have emerged into a ubiquitous tool in computer applications over the past 35 years, and they offer comprehensive capabilities for storing, retrieving, querying, and processing data that allow them to interact efficiently and appropriately with the information-system landscape found in present-day federated enterprise and Web based environments. They are standard software on virtually any computing platform, and they are increasingly used as an “embedded” component in both large and small (software) systems (e.g., workflow management systems, electronic commerce platforms, Web services, smart cards); they continue to grow in importance as more and more data needs to get stored in a way that supports efficient and application-oriented ways of processing. As the exploitation of database technology increases, the capabilities and functionality of database systems need to keep track. Advanced database systems try to meet the requirements of present-day database applications by offering advanced functionality in terms of data modeling, multimedia data type support, data integration capabilities, query languages, system features, and interfaces to other worlds. This article surveys the state-of-the-art in these areas.

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Home — Essay Samples — Information Science and Technology — Data Mining — Dbms Database


DBMS Database

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Table of contents

Database management system vs file system dbms file system, characteristics of database approach.

  • Representing the aspects of real-world application A Database represents the features of the real-world application.
  • According to this, change in the real world will be reflected in the database.
  • Managing Information An information is very helpful for whatever work we do so Database take care of its information. Management of information using Database makes us deliberated user of data.
  • Easy Operational Implementation Makes it Powerful All the operations like insert, delete, update, search etc used in the database are carried out in a flexible and easy way. A user with some knowledge can easily perform these operations which makes it powerful.
  • Self Describing Nature A database must be of self-describing itself, that narrate and describe itself and which contains a description of the whole database.
  • Logical Relationship Between Records And Data This helps the user to access various records depending upon the logical conditions by a single query from the database.

DBMS architecture data model

The role of data models, three perspectives, a data model instance may be one of three kinds according to ansi in 1975:.

  • Conceptual data model: describes the semantics of a domain, being the scope of the model. For example, it may be a model of the interest area of an organization or industry. This consists of entity classes, representing kinds of things of significance in the domain, and relationship assertions about associations between pairs of entity classes. A conceptual schema specifies the kinds of facts or propositions that can be expressed using the model. In that sense, it defines the allowed expressions in an artificial 'language' with a scope that is limited by the scope of the model.
  • Logic data model: describes the semantics, as represented by a particular data manipulation technology. This consists of descriptions of tables and columns, object-oriented classes, and XML tags, among other things.
  • Physical data model: describes the physical means by which data are stored. This is concerned with partitions, CPUs, tablespaces, and the like. The significance of this approach, according to ANSI, is that it allows the three perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual model.

Instances and schemas

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Advances in database systems education: Methods, tools, curricula, and way forward

Muhammad ishaq.

1 Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan

2 Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan

3 Department of Computer Science, University of Management and Technology, Lahore, Pakistan

Muhammad Shoaib Farooq

Muhammad faraz manzoor.

4 Department of Computer Science, Lahore Garrison University, Lahore, Pakistan

Uzma Farooq

Kamran abid.

5 Department of Electrical Engineering, University of the Punjab, Lahore, Pakistan

Mamoun Abu Helou

6 Faculty of Information Technology, Al Istiqlal University, Jericho, Palestine

Associated Data

Not Applicable.

Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students’ interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE.


Database systems play a pivotal role in the successful implementation of the information systems to ensure the smooth running of many different organizations and companies (Etemad & Küpçü, 2018 ; Morien, 2006 ). Therefore, at least one course about the fundamentals of database systems is taught in every computing and information systems degree (Nagataki et al., 2013 ). Database System Education (DSE) is concerned with different aspects of data management while developing software (Park et al., 2017 ). The IEEE/ACM computing curricula guidelines endorse 30–50 dedicated hours for teaching fundamentals of design and implementation of database systems so as to build a very strong theoretical and practical understanding of the DSE topics (Cvetanovic et al., 2010 ).

Practically, most of the universities offer one user-oriented course at undergraduate level that covers topics related to the data modeling and design, querying, and a limited number of hours on theory (Conklin & Heinrichs, 2005 ; Robbert & Ricardo, 2003 ), where it is often debatable whether to utilize a design-first or query-first approach. Furthermore, in order to update the course contents, some recent trends, including big data and the notion of NoSQL should also be introduced in this basic course (Dietrich et al., 2008 ; Garcia-Molina, 2008 ). Whereas, the graduate course is more theoretical and includes topics related to DB architecture, transactions, concurrency, reliability, distribution, parallelism, replication, query optimization, along with some specialized classes.

Researchers have designed a variety of tools for making different concepts of introductory database course more interesting and easier to teach and learn interactively (Brusilovsky et al., 2010 ) either using visual support (Nagataki et al., 2013 ), or with the help of gamification (Fisher & Khine, 2006 ). Similarly, the instructors have been improvising different methods to teach (Abid et al., 2015 ; Domínguez & Jaime, 2010 ) and evaluate (Kawash et al., 2020 ) this theoretical and practical course. Also, the emerging and hot topics such as cloud computing and big data has also created the need to revise the curriculum and methods to teach DSE (Manzoor et al., 2020 ).

The research in database systems education has evolved over the years with respect to modern contents influenced by technological advancements, supportive tools to engage the learners for better learning, and improvisations in teaching and assessment methods. Particularly, in recent years there is a shift from self-describing data-driven systems to a problem-driven paradigm that is the bottom-up approach where data exists before being designed. This mainly relies on scientific, quantitative, and empirical methods for building models, while pushing the boundaries of typical data management by involving mathematics, statistics, data mining, and machine learning, thus opening a multidisciplinary perspective. Hence, it is important to devote a few lectures to introducing the relevance of such advance topics.

Researchers have provided useful review articles on other areas including Introductory Programming Language (Mehmood et al., 2020 ), use of gamification (Obaid et al., 2020 ), research trends in the use of enterprise service bus (Aziz et al., 2020 ), and the role of IoT in agriculture (Farooq et al., 2019 , 2020 ) However, to the best of our knowledge, no such study was found in the area of database systems education. Therefore, this study discusses research work published in different areas of database systems education involving curricula, tools, and approaches that have been proposed to teach an introductory course on database systems in an effective manner. The rest of the article has been structured in the following manner: Sect.  2 presents related work and provides a comparison of the related surveys with this study. Section  3 presents the research methodology for this study. Section  4 analyses the major findings of the literature reviewed in this research and categorizes it into different important aspects. Section  5 represents advices for the instructors and future directions. Lastly, Sect.  6 concludes the article.

Related work

Systematic Literature Reviews have been found to be a very useful artifact for covering and understanding a domain. A number of interesting review studies have been found in different fields (Farooq et al., 2021 ; Ishaq et al., 2021 ). Review articles are generally categorized into narrative or traditional reviews (Abid et al., 2016 ; Ramzan et al., 2019 ), systematic literature review (Naeem et al., 2020 ) and meta reviews or mapping study (Aria & Cuccurullo, 2017 ; Cobo et al., 2012 ; Tehseen et al., 2020 ). This study presents a systematic literature review on database system education.

The database systems education has been discussed from many different perspectives which include teaching and learning methods, curriculum development, and the facilitation of instructors and students by developing different tools. For instance, a number of research articles have been published focusing on developing tools for teaching database systems course (Abut & Ozturk, 1997 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Furthermore, few authors have evaluated the DSE tools by conducting surveys and performing empirical experiments so as to gauge the effectiveness of these tools and their degree of acceptance among important stakeholders, teachers and students (Brusilovsky et al., 2010 ; Nelson & Fatimazahra, 2010 ). On the other hand, some case studies have also been discussed to evaluate the effectiveness of the improvised approaches and developed tools. For example, Regueras et al. ( 2007 ) presented a case study using the QUEST system, in which e-learning strategies are used to teach the database course at undergraduate level, while, Myers and Skinner ( 1997 ) identified the conflicts that arise when theories in text books regarding the development of databases do not work on specific applications.

Another important facet of DSE research focuses on the curriculum design and evolution for database systems, whereby (Alrumaih, 2016 ; Bhogal et al., 2012 ; Cvetanovic et al., 2010 ; Sahami et al., 2011 ) have proposed solutions for improvements in database curriculum for the better understanding of DSE among the students, while also keeping the evolving technology into the perspective. Similarly, Mingyu et al. ( 2017 ) have shared their experience in reforming the DSE curriculum by adding topics related to Big Data. A few authors have also developed and evaluated different tools to help the instructors teaching DSE.

There are further studies which focus on different aspects including specialized tools for specific topics in DSE (Mcintyre et al, 1995 ; Nelson & Fatimazahra, 2010 ). For instance, Mcintyre et al. ( 1995 ) conducted a survey about using state of the art software tools to teach advanced relational database design courses at Cleveland State University. However, the authors did not discuss the DSE curricula and pedagogy in their study. Similarly, a review has been conducted by Nelson and Fatimazahra ( 2010 ) to highlight the fact that the understanding of basic knowledge of database is important for students of the computer science domain as well as those belonging to other domains. They highlighted the issues encountered while teaching the database course in universities and suggested the instructors investigate these difficulties so as to make this course more effective for the students. Although authors have discussed and analyzed the tools to teach database, the tools are yet to be categorized according to different methods and research types within DSE. There also exists an interesting systematic mapping study by Taipalus and Seppänen ( 2020 ) that focuses on teaching SQL which is a specific topic of DSE. Whereby, they categorized the selected primary studies into six categories based on their research types. They utilized directed content analysis, such as, student errors in query formulation, characteristics and presentation of the exercise database, specific or non-specific teaching approach suggestions, patterns and visualization, and easing teacher workload.

Another relevant study that focuses on collaborative learning techniques to teach the database course has been conducted by Martin et al. ( 2013 ) This research discusses collaborative learning techniques and adapted it for the introductory database course at the Barcelona School of Informatics. The motive of the authors was to introduce active learning methods to improve learning and encourage the acquisition of competence. However, the focus of the study was only on a few methods for teaching the course of database systems, while other important perspectives, including database curricula, and tools for teaching DSE were not discussed in this study.

The above discussion shows that a considerable amount of research work has been conducted in the field of DSE to propose various teaching methods; develop and test different supportive tools, techniques, and strategies; and to improve the curricula for DSE. However, to the best of our knowledge, there is no study that puts all these relevant and pertinent aspects together while also classifying and discussing the supporting methods, and techniques. This review is considerably different from previous studies. Table ​ Table1 1 highlights the differences between this study and other relevant studies in the field of DSE using ✓ and – symbol reflecting "included" and "not included" respectively. Therefore, this study aims to conduct a systematic mapping study on DSE that focuses on compiling, classifying, and discussing the existing work related to pedagogy, supporting tools, and curricula.

Comparison with other related research articles

Research methodology

In order to preserve the principal aim of this study, which is to review the research conducted in the area of database systems education, a piece of advice has been collected from existing methods described in various studies (Elberzhager et al., 2012 ; Keele et al., 2007 ; Mushtaq et al., 2017 ) to search for the relevant papers. Thus, proper research objectives were formulated, and based on them appropriate research questions and search strategy were formulated as shown in Fig.  1 .

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Research objectives

The Following are the research objectives of this study:

  • i. To find high quality research work in DSE.
  • ii. To categorize different aspects of DSE covered by other researchers in the field.
  • iii. To provide a thorough discussion of the existing work in this study to provide useful information in the form of evolution, teaching guidelines, and future research directions of the instructors.

Research questions

In order to fulfill the research objectives, some relevant research questions have been formulated. These questions along with their motivations have been presented in Table ​ Table2 2 .

Study selection results

Search strategy

The Following search string used to find relevant articles to conduct this study. “Database” AND (“System” OR “Management”) AND (“Education*” OR “Train*” OR “Tech*” OR “Learn*” OR “Guide*” OR “Curricul*”).

Articles have been taken from different sources i.e. IEEE, Springer, ACM, Science Direct and other well-known journals and conferences such as Wiley Online Library, PLOS and ArXiv. The planning for search to find the primary study in the field of DSE is a vital task.

Study selection

A total of 29,370 initial studies were found. These articles went through a selection process, and two authors were designated to shortlist the articles based on the defined inclusion criteria as shown in Fig.  2 . Their conflicts were resolved by involving a third author; while the inclusion/exclusion criteria were also refined after resolving the conflicts as shown in Table ​ Table3. 3 . Cohen’s Kappa coefficient 0.89 was observed between the two authors who selected the articles, which reflects almost perfect agreement between them (Landis & Koch, 1977 ). While, the number of papers in different stages of the selection process for all involved portals has been presented in Table ​ Table4 4 .

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Selection criteria

Title based search: Papers that are irrelevant based on their title are manually excluded in the first stage. At this stage, there was a large portion of irrelevant papers. Only 609 papers remained after this stage.

Abstract based search: At this stage, abstracts of the selected papers in the previous stage are studied and the papers are categorized for the analysis along with research approach. After this stage only 152 papers were left.

Full text based analysis: Empirical quality of the selected articles in the previous stage is evaluated at this stage. The analysis of full text of the article has been conducted. The total of 70 papers were extracted from 152 papers for primary study. Following questions are defined for the conduction of final data extraction.

Quality assessment criteria

Following are the criteria used to assess the quality of the selected primary studies. This quality assessment was conducted by two authors as explained above.

  • The study focuses on curricula, tools, approach, or assessments in DSE, the possible answers were Yes (1), No (0)
  • The study presents a solution to the problem in DSE, the possible answers to this question were Yes (1), Partially (0.5), No (0)
  • The study focuses on empirical results, Yes (1), No (0)

Score pattern of publication channels

Almost 50.00% of papers had scored more than average and 33.33% of papers had scored between the average range i.e., 2.50–3.50. Some articles with the score below 2.50 have also been included in this study as they present some useful information and were published in education-based journals. Also, these studies discuss important demography and technology based aspects that are directly related to DSE.

Threats to validity

The validity of this study could be influenced by the following factors during the literature of this publication.

Construct validity

In this study this validity identifies the primary study for research (Elberzhager et al., 2012 ). To ensure that many primary studies have been included in this literature two authors have proposed possible search keywords in multiple repetitions. Search string is comprised of different terms related to DS and education. Though, list might be incomplete, count of final papers found can be changed by the alternative terms (Ampatzoglou et al., 2013 ). IEEE digital library, Science direct, ACM digital library, Wiley Online Library, PLOS, ArXiv and Google scholar are the main libraries where search is done. We believe according to the statistics of search engines of literature the most research can be found on these digital libraries (Garousi et al., 2013 ). Researchers also searched related papers in main DS research sites (VLDB, ICDM, EDBT) in order to minimize the risk of missing important publication.

Including the papers that does not belong to top journals or conferences may reduce the quality of primary studies in this research but it indicates that the representativeness of the primary studies is improved. However, certain papers which were not from the top publication sources are included because of their relativeness wisth the literature, even though they reduce the average score for primary studies. It also reduces the possibility of alteration of results which might have caused by the improper handling of duplicate papers. Some cases of duplications were found which were inspected later whether they were the same study or not. The two authors who have conducted the search has taken the final decision to the select the papers. If there is no agreement between then there must be discussion until an agreement is reached.

Internal validity

This validity deals with extraction and data analysis (Elberzhager et al., 2012 ). Two authors carried out the data extraction and primary studies classification. While the conflicts between them were resolved by involving a third author. The Kappa coefficient was 0.89, according to Landis and Koch ( 1977 ), this value indicates almost perfect level of agreement between the authors that reduces this threat significantly.

Conclusion validity

This threat deals with the identification of improper results which may cause the improper conclusions. In this case this threat deals with the factors like missing studies and wrong data extraction (Ampatzoglou et al., 2013 ). The objective of this is to limit these factors so that other authors can perform study and produce the proper conclusions (Elberzhager et al., 2012 ).

Interpretation of results might be affected by the selection and classification of primary studies and analyzing the selected study. Previous section has clearly described each step performed in primary study selection and data extraction activity to minimize this threat. The traceability between the result and data extracted was supported through the different charts. In our point of view, slight difference based on the publication selection and misclassification would not alter the main results.

External validity

This threat deals with the simplification of this research (Mateo et al., 2012 ). The results of this study were only considered that related to the DSE filed and validation of the conclusions extracted from this study only concerns the DSE context. The selected study representativeness was not affected because there was no restriction on time to find the published research. Therefore, this external validity threat is not valid in the context of this research. DS researchers can take search string and the paper classification scheme represented in this study as an initial point and more papers can be searched and categorized according to this scheme.

Analysis of compiled research articles

This section presents the analysis of the compiled research articles carefully selected for this study. It presents the findings with respect to the research questions described in Table ​ Table2 2 .

Selection results

A total of 70 papers were identified and analyzed for the answers of RQs described above. Table ​ Table6 6 represents a list of the nominated papers with detail of the classification results and their quality assessment scores.

Classification and quality assessment of selected articles

RQ1.Categorization of research work in DSE field

The analysis in this study reveals that the literature can be categorized as: Tools: any additional application that helps instructors in teaching and students in learning. Methods: any improvisation aimed at improving pedagogy or cognition. Curriculum: refers to the course content domains and their relative importance in a degree program, as shown in Fig.  3 .

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Taxonomy of DSE study types

Most of the articles provide a solution by gathering the data and also prove the novelty of their research through results. These papers are categorized as experiments w.r.t. their research types. Whereas, some of them case study papers which are used to generate an in depth, multifaceted understanding of a complex issue in its real-life context, while few others are review studies analyzing the previously used approaches. On the other hand, a majority of included articles have evaluated their results with the help of experiments, while others conducted reviews to establish an opinion as shown in Fig.  4 .

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Cross Mapping of DSE study type and research Types

Educational tools, especially those related to technology, are making their place in market faster than ever before (Calderon et al., 2011 ). The transition to active learning approaches, with the learner more engaged in the process rather than passively taking in information, necessitates a variety of tools to help ensure success. As with most educational initiatives, time should be taken to consider the goals of the activity, the type of learners, and the tools needed to meet the goals. Constant reassessment of tools is important to discover innovation and reforms that improve teaching and learning (Irby & Wilkerson, 2003 ). For this purpose, various type of educational tools such as, interactive, web-based and game based have been introduced to aid the instructors in order to explain the topic in more effective way.

The inclusion of technology into the classroom may help learners to compete in the competitive market when approaching the start of their career. It is important for the instructors to acknowledge that the students are more interested in using technology to learn database course instead of merely being taught traditional theory, project, and practice-based methods of teaching (Adams et al., 2004 ). Keeping these aspects in view many authors have done significant research which includes web-based and interactive tools to help the learners gain better understanding of basic database concepts.

Great research has been conducted with the focus of students learning. In this study we have discussed the students learning supportive with two major finding’s objectives i.e., tools which prove to be more helpful than other tools. Whereas, proposed tools with same outcome as traditional classroom environment. Such as, Abut and Ozturk ( 1997 ) proposed an interactive classroom environment to conduct database classes. The online tools such as electronic “Whiteboard”, electronic textbooks, advance telecommunication networks and few other resources such as Matlab and World Wide Web were the main highlights of their proposed smart classroom. Also, Pahl et al. ( 2004 ) presented an interactive multimedia-based system for the knowledge and skill oriented Web-based education of database course students. The authors had differentiated their proposed classroom environment from traditional classroom-based approach by using tool mediated independent learning and training in an authentic setting. On the other hand, some authors have also evaluated the educational tools based on their usage and impact on students’ learning. For example, Brusilovsky et al. ( 2010 )s evaluated the technical and conceptual difficulties of using several interactive educational tools in the context of a single course. A combined Exploratorium has been presented for database courses and an experimental platform, which delivers modified access to numerous types of interactive learning activities.

Also, Taipalus and Perälä ( 2019 ) investigated the types of errors that are persistent in writing SQL by the students. The authors also contemplated the errors while mapping them onto different query concepts. Moreover, Abelló Gamazo et al. ( 2016 ) presented a software tool for the e-assessment of relational database skills named LearnSQL. The proposed software allows the automatic and efficient e-learning and e-assessment of relational database skills. Apart from these, Yue ( 2013 ) proposed the database tool named Sakila as a unified platform to support instructions and multiple assignments of a graduate database course for five semesters. According to this study, students find this tool more useful and interesting than the highly simplified databases developed by the instructor, or obtained from textbook. On the other hand, authors have proposed tools with the main objective to help the student’s grip on the topic by addressing the pedagogical problems in using the educational tools. Connolly et al. ( 2005 ) discussed some of the pedagogical problems sustaining the development of a constructive learning environment using problem-based learning, a simulation game and interactive visualizations to help teach database analysis and design. Also, Yau and Karim ( 2003 ) proposed smart classroom with prevalent computing technology which will facilitate collaborative learning among the learners. The major aim of this smart classroom is to improve the quality of interaction between the instructors and students during lecture.

Student satisfaction is also an important factor for the educational tools to more effective. While it supports in students learning process it should also be flexible to achieve the student’s confidence by making it as per student’s needs (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Also, Cvetanovic et al. ( 2010 ) has proposed a web-based educational system named ADVICE. The proposed solution helps the students to reduce the gap between DBMS, theory and its practice. On the other hand, authors have enhanced the already existing educational tools in the traditional classroom environment to addressed the student’s concerns (Nelson & Fatimazahra, 2010 ; Regueras et al., 2007 ) Table ​ Table7 7 .

Tools: Adopted in DSE and their impacts

Hands on database development is the main concern in most of the institute as well as in industry. However, tools assisting the students in database development and query writing is still major concern especially in SQL (Brusilovsky et al., 2010 ; Nagataki et al., 2013 ).

Student’s grades reflect their conceptual clarity and database development skills. They are also important to secure jobs and scholarships after passing out, which is why it is important to have the educational learning tools to help the students to perform well in the exams (Cvetanovic et al., 2010 ; Taipalus et al., 2018 ). While, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Subsequently, existing educational tools needs to be upgraded or replaced by the more suitable assessment oriented interactive tools to attend challenging students needs (Pahl et al., 2004 ; Yuelan et al., 2011 ).

One other objective of developing the educational tools is to increase the interaction between the students and the instructors. In the modern era, almost every institute follows the student centered learning(SCL). In SCL the interaction between students and instructor increases with most of the interaction involves from the students. In order to support SCL the educational based interactive and web-based tools need to assign more roles to students than the instructors (Abbasi et al., 2016 ; Taipalus & Perälä, 2019 ; Yau & Karim, 2003 ).

Theory versus practice is still one of the main issues in DSE teaching methods. The traditional teaching method supports theory first and then the concepts learned in the theoretical lectures implemented in the lab. Whereas, others think that it is better to start by teaching how to write query, which should be followed by teaching the design principles for database, while a limited amount of credit hours are also allocated for the general database theory topics. This part of the article discusses different trends of teaching and learning style along with curriculum and assessments methods discussed in DSE literature.

A variety of teaching methods have been designed, experimented, and evaluated by different researchers (Yuelan et al., 2011 ; Chen et al., 2012 ; Connolly & Begg, 2006 ). Some authors have reformed teaching methods based on the requirements of modern way of delivering lectures such as Yuelan et al. ( 2011 ) reform teaching method by using various approaches e.g. a) Modern ways of education: includes multimedia sound, animation, and simulating the process and working of database systems to motivate and inspire the students. b) Project driven approach: aims to make the students familiar with system operations by implementing a project. c) Strengthening the experimental aspects: to help the students get a strong grip on the basic knowledge of database and also enable them to adopt a self-learning ability. d) Improving the traditional assessment method: the students should turn in their research and development work as the content of the exam, so that they can solve their problem on their own.

The main aim of any teaching method is to make student learn the subject effectively. Student must show interest in order to gain something from the lectures delivered by the instructors. For this, teaching methods should be interactive and interesting enough to develop the interest of the students in the subject. Students can show interest in the subject by asking more relative questions or completing the home task and assignments on time. Authors have proposed few teaching methods to make topic more interesting such as, Chen et al. ( 2012 ) proposed a scaffold concept mapping strategy, which considers a student’s prior knowledge, and provides flexible learning aids (scaffolding and fading) for reading and drawing concept maps. Also, Connolly & Begg (200s6) examined different problems in database analysis and design teaching, and proposed a teaching approach driven by principles found in the constructivist epistemology to overcome these problems. This constructivist approach is based on the cognitive apprenticeship model and project-based learning. Similarly, Domínguez & Jaime ( 2010 ) proposed an active method for database design through practical tasks development in a face-to-face course. They analyzed results of five academic years using quasi experimental. The first three years a traditional strategy was followed and a course management system was used as material repository. On the other hand, Dietrich and Urban ( 1996 ) have described the use of cooperative group learning concepts in support of an undergraduate database management course. They have designed the project deliverables in such a way that students develop skills for database implementation. Similarly, Zhang et al. ( 2018 ) have discussed several effective classroom teaching measures from the aspects of the innovation of teaching content, teaching methods, teaching evaluation and assessment methods. They have practiced the various teaching measures by implementing the database technologies and applications in Qinghai University. Moreover, Hou and Chen ( 2010 ) proposed a new teaching method based on blending learning theory, which merges traditional and constructivist methods. They adopted the method by applying the blending learning theory on Access Database programming course teaching.

Problem solving skills is a key aspect to any type of learning at any age. Student must possess this skill to tackle the hurdles in institute and also in industry. Create mind and innovative students find various and unique ways to solve the daily task which is why they are more likeable to secure good grades and jobs. Authors have been working to introduce teaching methods to develop problem solving skills in the students(Al-Shuaily, 2012 ; Cai & Gao, 2019 ; Martinez-González & Duffing, 2007 ; Gudivada et al., 2007 ). For instance, Al-Shuaily ( 2012 ) has explored four cognitive factors such as i) Novices’ ability in understanding, ii) Novices’ ability to translate, iii) Novice’s ability to write, iv) Novices’ skills that might influence SQL teaching, and learning methods and approaches. Also, Cai and Gao ( 2019 ) have reformed the teaching method in the database course of two higher education institutes in China. Skills and knowledge, innovation ability, and data abstraction were the main objective of their study. Similarly, Martinez-González and Duffing ( 2007 ) analyzed the impact of convergence of European Union (EU) in different universities across Europe. According to their study, these institutes need to restructure their degree program and teaching methodologies. Moreover, Gudivada et al. ( 2007 ) proposed a student’s learning method to work with the large datasets. they have used the Amazon Web Services API and.NET/C# application to extract a subset of the product database to enhance student learning in a relational database course.

On the other hand, authors have also evaluated the traditional teaching methods to enhance the problem-solving skills among the students(Eaglestone & Nunes, 2004 ; Wang & Chen, 2014 ; Efendiouglu & Yelken, 2010 ) Such as, Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a database design course at Sheffield University and discussed some of the issues they faced, regarding teaching, learning and assessments. Likewise, Wang and Chen ( 2014 ) summarized the problems mainly in teaching of the traditional database theory and application. According to the authors the teaching method is outdated and does not focus on the important combination of theory and practice. Moreover, Efendiouglu and Yelken ( 2010 ) investigated the effects of two different methods Programmed Instruction (PI) and Meaningful Learning (ML) on primary school teacher candidates’ academic achievements and attitudes toward computer-based education, and to define their views on these methods. The results show that PI is not favoured for teaching applications because of its behavioural structure Table ​ Table8 8 .

Methods: Teaching approaches adopted in DSE

Students become creative and innovative when the try to study on their own and also from different resources rather than curriculum books only. In the modern era, there are various resources available on both online and offline platforms. Modern teaching methods must emphasize on making the students independent from the curriculum books and educate them to learn independently(Amadio et al., 2003 ; Cai & Gao, 2019 ; Martin et al., 2013 ). Also, in the work of Kawash et al. ( 2020 ) proposed he group study-based learning approach called Graded Group Activities (GGAs). In this method students team up in order to take the exam as a group. On the other hand, few studies have emphasized on course content to prepare students for the final exams such as, Zheng and Dong ( 2011 ) have discussed the issues of computer science teaching with particular focus on database systems, where different characteristics of the course, teaching content and suggestions to teach this course effectively have been presented.

As technology is evolving at rapid speed, so students need to have practical experience from the start. Basic theoretical concepts of database are important but they are of no use without its implementation in real world projects. Most of the students study in the institutes with the aim of only clearing the exams with the help of theoretical knowledge and very few students want to have practical experience(Wang & Chen, 2014 ; Zheng & Dong, 2011 ). To reduce the gap between the theory and its implementation, authors have proposed teaching methods to develop the student’s interest in the real-world projects (Naik & Gajjar, 2021 ; Svahnberg et al., 2008 ; Taipalus et al., 2018 ). Moreover, Juxiang and Zhihong ( 2012 ) have proposed that the teaching organization starts from application scenarios, and associate database theoretical knowledge with the process from analysis, modeling to establishing database application. Also, Svahnberg et al. ( 2008 ) explained that in particular conditions, there is a possibility to use students as subjects for experimental studies in DSE and influencing them by providing responses that are in line with industrial practice.

On the other hand, Nelson et al. ( 2003 ) evaluated the different teaching methods used to teach different modules of database in the School of Computing and Technology at the University of Sunder- land. They outlined suggestions for changes to the database curriculum to further integrate research and state-of-the-art systems in databases.

  • III. Curriculum

Database curriculum has been revisited many times in the form of guidelines that not only present the contents but also suggest approximate time to cover different topics. According to the ACM curriculum guidelines (Lunt et al., 2008 ) for the undergraduate programs in computer science, the overall coverage time for this course is 46.50 h distributed in such a way that 11 h is the total coverage time for the core topics such as, Information Models (4 core hours), Database Systems (3 core hours) and Data Modeling (4 course hours). Whereas, the remaining hours are allocated for elective topics such as Indexing, Relational Databases, Query Languages, Relational Database Design, Transaction Processing, Distributed Databases, Physical Database Design, Data Mining, Information Storage and Retrieval, Hypermedia, Multimedia Systems, and Digital Libraries(Marshall, 2012 ). While, according to the ACM curriculum guidelines ( 2013 ) for undergraduate programs in computer science, this course should be completed in 15 weeks with two and half hour lecture per week and lab session of four hours per week on average (Brady et al., 2004 ). Thus, the revised version emphasizes on the practice based learning with the help of lab component. Numerous organizations have exerted efforts in this field to classify DSE (Dietrich et al., 2008 ). DSE model curricula, bodies of knowledge (BOKs), and some standardization aspects in this field are discussed below:

Model curricula

There are standard bodies who set the curriculum guidelines for teaching undergraduate degree programs in computing disciplines. Curricula which include the guidelines to teach database are: Computer Engineering Curricula (CEC) (Meier et al., 2008 ), Information Technology Curricula (ITC) (Alrumaih, 2016 ), Computing Curriculum Software Engineering (CCSE) (Meyer, 2001 ), Cyber Security Curricula (CSC) (Brady et al., 2004 ; Bishop et al., 2017 ).

Bodies of knowledge (BOK)

A BOK includes the set of thoughts and activities related to the professional area, while in model curriculum set of guidelines are given to address the education issues (Sahami et al., 2011 ). Database body of Knowledge comprises of (a) The Data Management Body of Knowledge (DM- BOK), (b) Software Engineering Education Knowledge (SEEK) (Sobel, 2003 ) (Sobel, 2003 ), and (c) The SE body of knowledge (SWEBOK) (Swebok Evolution: IEEE Computer Society n.d. ).

Apart from the model curricula, and bodies of knowledge, there also exist some standards related to the database and its different modules: ISO/IEC 9075–1:2016 (Computing Curricula, 1991 ), ISO/IEC 10,026–1: 1998 (Suryn, 2003 ).

We also utilize advices from some studies (Elberzhager et al., 2012 ; Keele et al., 2007 ) to search for relevant papers. In order to conduct this systematic study, it is essential to formulate the primary research questions (Mushtaq et al., 2017 ). Since the data management techniques and software are evolving rapidly, the database curriculum should also be updated accordingly to meet these new requirements. Some authors have described ways of updating the content of courses to keep pace with specific developments in the field and others have developed new database curricula to keep up with the new data management techniques.

Furthermore, some authors have suggested updates for the database curriculum based on the continuously evolving technology and introduction of big data. For instance Bhogal et al. ( 2012 ) have shown that database curricula need to be updated and modernized, which can be achieved by extending the current database concepts that cover the strategies to handle the ever changing user requirements and how database technology has evolved to meet the requirements. Likewise, Picciano ( 2012 ) examines the evolving world of big data and analytics in American higher education. According to the author, the “data driven” decision making method should be used to help the institutes evaluate strategies that can improve retention and update the curriculum that has big data basic concepts and applications, since data driven decision making has already entered in the big data and learning analytic era. Furthermore, Marshall ( 2011 ) presented the challenges faced when developing a curriculum for a Computer Science degree program in the South African context that is earmarked for international recognition. According to the author, the Curricula needs to adhere both to the policy and content requirements in order to be rated as being of a particular quality.

Similarly, some studies (Abourezq & Idrissi, 2016 ; Mingyu et al., 2017 ) described big data influence from a social perspective and also proceeded with the gaps in database curriculum of computer science, especially, in the big data era and discovers the teaching improvements in practical and theoretical teaching mode, teaching content and teaching practice platform in database curriculum. Also Silva et al. ( 2016 ) propose teaching SQL as a general language that can be used in a wide range of database systems from traditional relational database management systems to big data systems.

On the other hand, different authors have developed a database curriculum based on the different academic background of students. Such as, Dean and Milani ( 1995 ) have recommended changes in computer science curricula based on the practice in United Stated Military Academy (USMA). They emphasized greatly on the practical demonstration of the topic rather than the theoretical explanation. Especially, for the non-computer science major students. Furthermore, Urban and Dietrich ( 2001 ) described the development of a second course on database systems for undergraduates, preparing students for the advanced database concepts that they will exercise in the industry. They also shared their experience with teaching the course, elaborating on the topics and assignments. Also, Andersson et al. ( 2019 ) proposed variations in core topics of database management course for the students with the engineering background. Moreover, Dietrich et al. ( 2014 ) described two animations developed with images and color that visually and dynamically introduce fundamental relational database concepts and querying to students of many majors. The goal is that the educators, in diverse academic disciplines, should be able to incorporate these animations in their existing courses to meet their pedagogical needs.

The information systems have evolved into large scale distributed systems that store and process a huge amount of data across different servers, and process them using different distributed data processing frameworks. This evolution has given birth to new paradigms in database systems domain termed as NoSQL and Big Data systems, which significantly deviate from conventional relational and distributed database management systems. It is pertinent to mention that in order to offer a sustainable and practical CS education, these new paradigms and methodologies as shown in Fig.  5 should be included into database education (Kleiner, 2015 ). Tables ​ Tables9 9 and ​ and10 10 shows the summarized findings of the curriculum based reviewed studies. This section also proposed appropriate text book based on the theory, project, and practice-based teaching methodology as shown in Table ​ Table9. 9 . The proposed books are selected purely on the bases of their usage in top universities around the world such as, Massachusetts Institute of Technology, Stanford University, Harvard University, University of Oxford, University of Cambridge and, University of Singapore and the coverage of core topics mentioned in the database curriculum.

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Concepts in Database Systems Education (Kleiner, 2015 )

Recommended text books for DSE

Curriculum: Findings of Reviewed Literature

RQ.2 Evolution of DSE research

This section discusses the evolution of database while focusing the DSE over the past 25 years as shown in Fig.  6 .

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Evolution of DSE studies

This study shows that there is significant increase in research in DSE after 2004 with 78% of the selected papers are published after 2004. The main reason of this outcome is that some of the papers are published in well-recognized channels like IEEE Transactions on Education, ACM Transactions on Computing Education, International Conference on Computer Science and Education (ICCSE), and Teaching, Learning and Assessment of Database (TLAD) workshop. It is also evident that several of these papers were published before 2004 and only a few articles were published during late 1990s. This is because of the fact that DSE started to gain interest after the introduction of Body of Knowledge and DSE standards. The data intensive scientific discovery has been discussed as the fourth paradigm (Hey et al., 2009 ): where the first involves empirical science and observations; second contains theoretical science and mathematically driven insights; third considers computational science and simulation driven insights; while the fourth involves data driven insights of modern scientific research.

Over the past few decades, students have gone from attending one-room class to having the world at their fingertips, and it is a great challenge for the instructors to develop the interest of students in learning database. This challenge has led to the development of the different types of interactive tools to help the instructors teach DSE in this technology oriented era. Keeping the importance of interactive tools in DSE in perspective, various authors have proposed different interactive tools over the years, such as during 1995–2003, when different authors proposed various interactive tools. Some studies (Abut & Ozturk, 1997 ; Mcintyre et al., 1995 ) introduced state of the art interactive tools to teach and enhance the collaborative learning among the students. Similarly, during 2004–2005 more interactive tools in the field of DSE were proposed such as Pahl et al. ( 2004 ), Connolly et al. ( 2005 ) introduced multimedia system based interactive model and game based collaborative learning environment.

The Internet has started to become more common in the first decade of the twenty-first century and its positive impact on the education sector was undeniable. Cost effective, student teacher peer interaction, keeping in touch with the latest information were the main reasons which made the instructors employ web-based tools to teach database in the education sector. Due to this spike in the demand of web-based tools, authors also started to introduce new instruments to assist with teaching database. In 2007 Regueras et al. ( 2007 ) proposed an e-learning tool named QUEST with a feedback module to help the students to learn from their mistakes. Similarly, in 2010, multiple authors have proposed and evaluated various web-based tools. Cvetanovic et al. ( 2010 ) proposed ADVICE with the functionality to monitor student’s progress, while, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Furthermore, Nelson and Fatimazahra ( 2010 ) evaluated different web-based tools to highlight the complexities of using these web-based instruments.

Technology has changed the teaching methods in the education sector but technology cannot replace teachers, and despite the amount of time most students spend online, virtual learning will never recreate the teacher-student bond. In the modern era, innovation in technology used in educational sectors is not meant to replace the instructors or teaching methods.

During the 1990s some studies (Dietrich & Urban, 1996 ; Urban & Dietrich, 1997 ) proposed learning and teaching methods respectively keeping the evolving technology in view. The highlight of their work was project deliverables and assignments where students progressively advanced to a step-by-step extension, from a tutorial exercise and then attempting more difficult extension of assignment.

During 2002–2007 various authors have discussed a number of teaching and learning methods to keep up the pace with the ever changing database technology, such as Connolly and Begg ( 2006 ) proposing a constructive approach to teach database analysis and design. Similarly, Prince and Felder ( 2006 ) reviewed the effectiveness of inquiry learning, problem based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. Also, McIntyre et al. (Mcintyre et al., 1995 ) brought to light the impact of convergence of European Union (EU) in different universities across Europe. They suggested a reconstruction of teaching and learning methodologies in order to effectively teach database.

During 2008–2013 more work had been done to address the different methods of teaching and learning in the field of DSE, like the work of Dominguez and Jaime ( 2010 ) who proposed an active learning approach. The focus of their study was to develop the interest of students in designing and developing databases. Also, Zheng and Dong ( 2011 ) have highlighted various characteristics of the database course and its teaching content. Similarly, Yuelan et al. ( 2011 ) have reformed database teaching methods. The main focus of their study were the Modern ways of education, project driven approach, strengthening the experimental aspects, and improving the traditional assessment method. Likewise, Al-Shuaily ( 2012 ) has explored 4 cognitive factors that can affect the learning process of database. The main focus of their study was to facilitate the students in learning SQL. Subsequently, Chen et al. ( 2012 ) also proposed scaffolding-based concept mapping strategy. This strategy helps the students to better understand database management courses. Correspondingly, Martin et al. ( 2013 ) discussed various collaborative learning techniques in the field of DSE while keeping database as an introductory course.

In the years between 2014 and 2021, research in the field of DSE increased, which was the main reason that the most of teaching, learning and assessment methods were proposed and discussed during this period. Rashid and Al-Radhy ( 2014 ) discussed the issues of traditional teaching, learning, assessing methods of database courses at different universities in Kurdistan and the main focus of their study being reformation issues, such as absence of teaching determination and contradiction between content and theory. Similarly, Wang and Chen ( 2014 ) summarized the main problems in teaching the traditional database theory and its application. Curriculum assessment mode was the main focus of their study. Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a databases design course at Sheffield University. Their focus of study included was to teach the database design module to a diverse group of students from different backgrounds. Rashid ( 2015 ) discussed some important features of database courses, whereby reforming the conventional teaching, learning, and assessing strategies of database courses at universities were the main focus of this study. Kui et al. ( 2018 ) reformed the teaching mode of database courses based on flipped classroom. Initiative learning of database courses was their main focus in this study. Similarly, Zhang et al. ( 2018 ) discussed several effective classroom teaching measures. The main focus of their study was teaching content, teaching methods, teaching evaluation and assessment methods. Cai and Gao ( 2019 ) also carried out the teaching reforms in the database course of liberal arts. Diversified teaching modes, such as flipping classroom, case oriented teaching and task oriented were the focus of their study. Teaching Kawash et al. ( 2020 ) proposed a learning approach called Graded Group Activities (GGAs). Their main focus of the study was reforming learning and assessment method.

Database course covers several topics that range from data modeling to data implementation and examination. Over the years, various authors have given their suggestions to update these topics in database curriculum to meet the requirements of modern technologies. On the other hand, authors have also proposed a new curriculum for the students of different academic backgrounds and different areas. These reformations in curriculum helped the students in their preparation, practically and theoretically, and enabled them to compete in the competitive market after graduation.

During 2003 and 2006 authors have proposed various suggestions to update and develop computer science curriculum across different universities. Robbert and Ricardo ( 2003 ) evaluated three reviews from 1999 to 2002 that were given to the groups of educators. The focus of their study was to highlight the trends that occurred in database curriculum. Also, Calero et al. ( 2003 ) proposed a first draft for this Database Body of Knowledge (DBBOK). Database (DB), Database Design (DBD), Database Administration (DBAd), Database Application (DBAp) and Advance Databases (ADVDB) were the main focus of their study. Furthermore, Conklin and Heinrichs (Conklin & Heinrichs, 2005 ) compared the content included in 13 database textbooks and the main focus of their study was IS 2002, CC2001, and CC2004 model curricula.

The years from 2007 and 2011, authors managed to developed various database curricula, like Luo et al. ( 2008 ) developed curricula in Zhejiang University City College. The aim of their study to nurture students to be qualified computer scientists. Likewise, Dietrich et al. ( 2008 ) proposed the techniques to assess the development of an advanced database course. The purpose behind the addition of an advanced database course at undergraduate level was to prepare the students to respond to industrial requirements. Also, Marshall ( 2011 ) developed a new database curriculum for Computer Science degree program in the South African context.

During 2012 and 2021 various authors suggested updates for the database curriculum such as Bhogal et al. ( 2012 ) who suggested updating and modernizing the database curriculum. Data management and data analytics were the focus of their study. Similarly, Picciano ( 2012 ) examined the curriculum in the higher level of American education. The focus of their study was big data and analytics. Also, Zhanquan et al. ( 2016 ) proposed the design for the course content and teaching methods in the classroom. Massive Open Online Courses (MOOCs) were the focus of their study. Likewise, Mingyu et al. ( 2017 ) suggested updating the database curriculum while keeping new technology concerning the database in perspective. The focus of their study was big data.

The above discussion clearly shows that the SQL is most discussed topic in the literature where more than 25% of the studies have discussed it in the previous decade as shown in Fig.  7 . It is pertinent to mention that other SQL databases such as Oracle, MS access are discussed under the SQL banner (Chen et al., 2012 ; Hou & Chen, 2010 ; Wang & Chen, 2014 ). It is mainly because of its ability to handle data in a relational database management system and direct implementation of database theoretical concepts. Also, other database topics such as transaction management, application programming etc. are also the main highlights of the topics discussed in the literature.

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Evolution of Database topics discussed in literature

Research synthesis, advice for instructors, and way forward

This section presents the synthesized information extracted after reading and analyzing the research articles considered in this study. To this end, it firstly contextualizes the tools and methods to help the instructors find suitable tools and methods for their settings. Similarly, developments in curriculum design have also been discussed. Subsequently, general advice for instructors have been discussed. Lastly, promising future research directions for developing new tools, methods, and for revising the curriculum have also been discussed in this section.

Methods, tools, and curriculum

Methods and tools.

Web-based tools proposed by Cvetanovic et al. ( 2010 ) and Wang et al. ( 2010 ) have been quite useful, as they are growing increasingly pertinent as online mode of education is prevalent all around the globe during COVID-19. On the other hand, interactive tools and smart class room methodology has also been used successfully to develop the interest of students in database class. (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ; Canedo et al., 2021 ; Ko et al., 2021 ).

One of the most promising combination of methodology and tool has been proposed by Cvetanovic et al. ( 2010 ), whereby they developed a tool named ADVICE that helps students learn and implement database concepts while using project centric methodology, while a game based collaborative learning environment was proposed by Connolly et al. ( 2005 ) that involves a methodology comprising of modeling, articulation, feedback, and exploration. As a whole, project centric teaching (Connolly & Begg, 2006 ; Domínguez & Jaime, 2010 ) and teaching database design and problem solving skills Wang and Chen ( 2014 ), are two successful approaches for DSE. Whereas, other studies (Urban & Dietrich, 1997 ) proposed teaching methods that are more inclined towards practicing database concepts. While a topic specific approach has been proposed by Abbasi et al. ( 2016 ), Taipalus et al. ( 2018 ) and Silva et al. ( 2016 ) to teach and learn SQL. On the other hand, Cai and Gao ( 2019 ) developed a teaching method for students who do not have a computer science background. Lastly, some useful ways for defining assessments for DSE have been proposed by Kawash et al. ( 2020 ) and Zhang et al. ( 2018 ).

Curriculum of database adopted by various institutes around the world does not address how to teach the database course to the students who do not have a strong computer science background. Such as Marshall ( 2012 ), Luo et al. ( 2008 ) and Zhanquan et al. ( 2016 ) have proposed the updates in current database curriculum for the students who are not from computer science background. While Abid et al. ( 2015 ) proposed a combined course content and various methodologies that can be used for teaching database systems course. On the other hand, current database curriculum does not include the topics related to latest technologies in database domain. This factor was discussed by many other studies as well (Bhogal et al., 2012 ; Mehmood et al., 2020 ; Picciano, 2012 ).

Guidelines for instructors

The major conclusion of this study are the suggestions based on the impact and importance for instructors who are teaching DSE. Furthermore, an overview of productivity of every method can be provided by the empirical studies. These instructions are for instructors which are the focal audience of this study. These suggestions are subjective opinions after literature analysis in form of guidelines according to the authors and their meaning and purpose were maintained. According to the literature reviewed, various issues have been found in this section. Some other issues were also found, but those were not relevant to DSE. Following are some suggestions that provide interesting information:

Project centric and applied approach

  • To inculcate database development skills for the students, basic elements of database development need to be incorporated into teaching and learning at all levels including undergraduate studies (Bakar et al., 2011 ). To fulfill this objective, instructors should also improve the data quality in DSE by assigning the projects and assignments to the students where they can assess, measure and improve the data quality using already deployed databases. They should demonstrate that the quality of data is determined not only by the effective design of a database, but also through the perception of the end user (Mathieu & Khalil, 1997 )
  • The gap between the database course theory and industrial practice is big. Fresh graduate students find it difficult to cope up with the industrial pressure because of the contrast between what they have been taught in institutes and its application in industry (Allsopp et al., 2006 ). Involve top performers from classes in industrial projects so that they are able to acquiring sufficient knowledge and practice, especially for post graduate courses. There must be some other activities in which industry practitioners come and present the real projects and also share their industrial experiences with the students. The gap between theoretical and the practical sides of database has been identified by Myers and Skinner ( 1997 ). In order to build practical DS concepts, instructors should provide the students an accurate view of reality and proper tools.

Importance of software development standards and impact of DB in software success

  • They should have the strategies, ability and skills that can align the DSE course with the contemporary Global Software Development (GSD) (Akbar & Safdar, 2015 ; Damian et al., 2006 ).
  • Enable the students to explain the approaches to problem solving, development tools and methodologies. Also, the DS courses are usually taught in normal lecture format. The result of this method is that students cannot see the influence on the success or failure of projects because they do not realize the importance of DS activities.

Pedagogy and the use of education technology

  • Some studies have shown that teaching through play and practical activities helps to improve the knowledge and learning outcome of students (Dicheva et al., 2015 ).
  • Interactive classrooms can help the instructors to deliver their lecture in a more effective way by using virtual white board, digital textbooks, and data over network(Abut & Ozturk, 1997 ). We suggest that in order to follow the new concept of smart classroom, instructors should use the experience of Yau and Karim ( 2003 ) which benefits in cooperative learning among students and can also be adopted in DSE.
  • The instructors also need to update themselves with full spectrum of technology in education, in general, and for DSE, in particular. This is becoming more imperative as during COVID the world is relying strongly on the use of technology, particularly in education sector.

Periodic Curriculum Revision

  • There is also a need to revisit the existing series of courses periodically, so that they are able to offer the following benefits: (a) include the modern day database system concepts; (b) can be offered as a specialization track; (c) a specialized undergraduate degree program may also be designed.

DSE: Way forward

This research combines a significant work done on DSE at one place, thus providing a point to find better ways forward in order to improvise different possible dimensions for improving the teaching process of a database system course in future. This section discusses technology, methods, and modifications in curriculum would most impact the delivery of lectures in coming years.

Several tools have already been developed for effective teaching and learning in database systems. However, there is a great room for developing new tools. Recent rise of the notion of “serious games” is marking its success in several domains. Majority of the research work discussed in this review revolves around web-based tools. The success of serious games invites researchers to explore this new paradigm of developing useful tools for learning and practice database systems concepts.

Likewise, due to COVID-19 the world is setting up new norms, which are expected to affect the methods of teaching as well. This invites the researchers to design, develop, and test flexible tools for online teaching in a more interactive manner. At the same time, it is also imperative to devise new techniques for assessments, especially conducting online exams at massive scale. Moreover, the researchers can implement the idea of instructional design in web-based teaching in which an online classroom can be designed around the learners’ unique backgrounds and effectively delivering the concepts that are considered to be highly important by the instructors.

The teaching, learning and assessment methods discussed in this study can help the instructors to improve their methods in order to teach the database system course in a better way. It is noticed that only 16% of authors have the assessment methods as their focus of study, which clearly highlights that there is still plenty of work needed to be done in this particular domain. Assessment techniques in the database course will help the learners to learn from their mistakes. Also, instructors must realize that there is a massive gap between database theory and practice which can only be reduced with maximum practice and real world database projects.

Similarly, the technology is continuously influencing the development and expansion of modern education, whereas the instructors’ abilities to teach using online platforms are critical to the quality of online education.

In the same way, the ideas like flipped classroom in which students have to prepare the lesson prior to the class can be implemented on web-based teaching. This ensures that the class time can be used for further discussion of the lesson, share ideas and allow students to interact in a dynamic learning environment.

The increasing impact of big data systems, and data science and its anticipated impact on the job market invites the researchers to revisit the fundamental course of database systems as well. There is a need to extend the boundaries of existing contents by including the concepts related to distributed big data systems data storage, processing, and transaction management, with possible glimpse of modern tools and technologies.

As a whole, an interesting and long term extension is to establish a generic and comprehensive framework that engages all the stakeholders with the support of technology to make the teaching, learning, practicing, and assessing easier and more effective.

This SLR presents review on the research work published in the area of database system education, with particular focus on teaching the first course in database systems. The study was carried out by systematically selecting research papers published between 1995 and 2021. Based on the study, a high level categorization presents a taxonomy of the published under the heads of Tools, Methods, and Curriculum. All the selected articles were evaluated on the basis of a quality criteria. Several methods have been developed to effectively teach the database course. These methods focus on improving learning experience, improve student satisfaction, improve students’ course performance, or support the instructors. Similarly, many tools have been developed, whereby some tools are topic based, while others are general purpose tools that apply for whole course. Similarly, the curriculum development activities have also been discussed, where some guidelines provided by ACM/IEEE along with certain standards have been discussed. Apart from this, the evolution in these three areas has also been presented which shows that the researchers have been presenting many different teaching methods throughout the selected period; however, there is a decrease in research articles that address the curriculum and tools in the past five years. Besides, some guidelines for the instructors have also been shared. Also, this SLR proposes a way forward in DSE by emphasizing on the tools: that need to be developed to facilitate instructors and students especially post Covid-19 era, methods: to be adopted by the instructors to close the gap between the theory and practical, Database curricula update after the introduction of emerging technologies such as big data and data science. We also urge that the recognized publication venues for database research including VLDB, ICDM, EDBT should also consider publishing articles related to DSE. The study also highlights the importance of reviving the curricula, tools, and methodologies to cater for recent advancements in the field of database systems.

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Database Management System Essays

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Database Systems & Open Sourcing Essay

A database system refers to a pool of information that is logically arranged in a sequential manner. A database system may also mean a data model that encompasses a database management system (DBMS). Data is normally stored in the data structures or a database system, a database management system (DBMS), in a computer acts like a shell under which all interaction of information takes place within the various databases. DBMS may also be referred to as a software package that has computer programs that maintain the creation and use of the database (Kumar 2007).

The DBMS is a convenient model as it allows different users to access programs that ran concurrently in the same database. DBMS is compatible and can support a variety of database models like objective model or the relational model which allows it to describe and also support applications. The above-mentioned ability by DBMS allows it to conveniently support query language. This is a high-level programming language that is typically used as a simplified database language in writing of database application programs. Database language also comes in handy in database organization such as retrieving and presenting of information.

From the tutorials, one learns that the DBMS in a computer offers other important access facilities like ensuring data integrity, facilitating for concurrency control, controlling data access and transfer of data. It also aids in the recovery of data either due to a failure in the system or when restoring data through a back up file. It is important to mention that DBMS are generally categorized under different levels; these levels are determined by their data structure, for example DBMS receives request for information from any program, DBMS on its part instructs what is referred to as the operating system to relay appropriate data, but this can only happen if the data requested adheres to a certain conformity, that is, data should be under an applicable protocol. On the other hand, database servers are multiprocessors computers that have a high memory; this is complemented by a RAID disk that allows for stable storage, but in modern day DBMS relies mostly on standard operating system (Kumar 2007)

A database system is commonly used to keep track of information, such as username, phone number, login information and their various addresses. For instances, in terms of navigation approaches, all data that relates to it is placed under a single record, those that are not in use are normally omitted from the database. If the database system is under the relational approach, the data is usually normalized to form a user table.

On the other hand, over the years, open source has been in a tug of war over its ownership with different groups claiming it, but the term “open source ” has never been categorized as a trademark. Thus, over the years, its definition was recognized as a standard or what may be referred to as de facto definition. Open source software is designed computer software that is made available in a source cord form. Normally, it is designed and developed through a collaborative manner. It is the best example of an open source development and it often compares to open content and at times to user generated content (Silberschatz, 1999). Open source software normally outlines the terms of usage, redistribution and modification of open source software. This is precipitated by the need by software developers to try to get software licenses. This allows users to be granted rights, which would otherwise be held by copyright laws. There are a number of open source software that have qualified to be licensed under the definition, these include the GNU General Public License (GPL) which allows for free distribution although with a catch that any other future development of application should only be under the same license. It is prudent to mention that although the open distribution offers the source cord of a product for access by the public. Open source license gives the authors the ability to fine-tune such access. There is a need to distinguish between open source and free software movement, open source is by and large associated with the launch of internet, but the free software foundation (FSF) intended connotation “free” to mean its distribution was free, but not freedom from cost, thus being viewed as being anti-commercial, but this is not entirely true (Silberschatz.1999)

Open source software is mainly maintained through a network of voluntary programs. Some of the widely known open source products are; the internet browser Mozilla Firefox, the Apache HTTP server, and the e-commerce platform (SQL Video Series, 2010). There are other successful open source products in the market like the GNU/Linux operating system. This has seen other major players like Microsoft venture into open source with the adoption of the open document format and also the creation of other open standard that can open the XML format.

In conclusion, it is worth noting that the tutorial is an excellent one in terms of its ability to simplify the intended concept. The tutorial is easy to understand as it gives clear step to step processes that are required in various aspect of manipulating the databases. Simply put, the tutorial effectively manages to simplify what is otherwise considered complex easily and elaborately. Therefore, the tutorial is beneficial to anyone who is a starter in the course, it offers a wide array of information about how to start a database system, how to operate it, pros and cons in terms of data management, distribution, storage and upgrading. Also, the tutorial allows for a learner to gain knowledge and skill of how the different database systems and open source software facilitate redistribution of data to users. In addition, the knowledge gained allows for innovative development of software which in itself is a form of employment.

Kumar, P., S. (2007) Database Systems Introduction CS&E Department I I T Madras. Web.

Silberschatz. A., Korthand, H., F. and Sudarshan, S. (1999). Database system concepts: McGraw-Hill series in computer science. Michigan: McGraw-Hill,

SQL Video Series, (2011). SQL Server 2005 Express Edition for Beginners. Web.

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Database Management Systems (DBMS), Essay Example

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The course topic I have selected to investigate for this week’s discussion is database management systems (DBMS). The most common type of search engines that are used today are crawler-based search engines, which create their listings automatically by allowing their software to search the web and list its findings to comprise a database. Search engines that use this technique include Google, AllTheWeb, and AltaVista. The first search that I conducted on Google simply included the search term “DBMS” and the first website that it listed was a Wikipedia page. This is because this is the most visited and linked to website on the topic. Wikipedia briefly explained that there are four different types of DBMS, including hierarchal, network, relational, and object-oriented. Other search engines utilize human-powered directories, and these include Yahoo directory, Open Directory, and LookSmart. These search engines are useful when a user is interested in a particular topic and wishes to retrieve a description of the information during the search. A majority of technology and science databases, such as PubMed, also use this method. Thus, results retrieved are more relevant. A Yahoo directory search of “DBMS” brought up a definition of the term on a website call “Techterms.com”. It reads “Stands for “Database Management System.” In short, a DBMS is a database program.” This search result is more relevant than the first, as the Wikipedia article only listed types of DBMS rather explaining what DBMS actually is. Meta-search engines include Dogpile, Mamma, and Metacrawler, and search several keywords at the same time. These engines are ideal for saving time because the user can search several different search engines by using them. A search of “DBMS” on Dogpile brought up the main hits from both the Google search and the Yahoo directory search. Although DBMS is a somewhat common term, it can be expected that this is a reasonable search method when a term has less relevant pages and a more thorough search is therefore required.

Ultimately, I prefer Google for simple knowledge searches, Yahoo directory to answer specific research questions, and Dogpile to acquire information about uncommon topics. The search results I encountered were expected because I searched using one term rather than several. If I had searched more than one term using Dogpile, it is likely that I would have received results pertaining to both topics at once and both topics individually. In Yahoo directory, the results would have only pertained to one topic. Fortunately, I did not encounter any problems during this exercise.

Stanger J, Kozakis KA. (2012). Internet Business Associate Academic Student Guide. Tempe,  AZ: Certification Partners, LLC.

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The entity-relationship model (or ERE model) is a way of graphically representing the logical relationships of entitles (or objects) In order to create a database. Entity type Is a collection of entity Instances sharing scalar properties Strong Entity Vs. Weak Entity An entity set that does not have sufficient attributes to form a primary key is termed as a weak entity set. An entity set that has a primary key is termed as strong entity set.

A weak entity Is existence dependent. That Is the existence of a weak entity depends on the existence of a Identifying entity set.The discriminator (or partial key) Is used o identify other attributes of a weak entity set. The primary key of a weak entity set is formed by primary key of identifying entity set and the discriminator of weak entity set. The existence of a weak entity is indicated by a double rectangle in the ERE diagram.

We underline the discriminator of a weak entity set with a dashed line in the ERE diagram. Recursive Entity A recursive entity is one in which a relation can exist between occurrences of the same entity set.This occurs in a unary relationship. Composite Entities If a Many to Many relationship exist we must create a bridge entity to convert It Into 1 o Many. Bridge entity composed of the primary keys of each of the entitles to be connected. The bridge entity is known as a composite entity.

A composite entity is represented by a diamond shape with in a rectangle in an ERE Diagram. Types of Relationships Unary Relationship ENTITY TYPE linked with itself, also called recursive relationship.Counterexample, where STUDENT is linked with STUDENT Binary relationships Binary relationship Is the one that links two entitles sets e. G.

STUDENT-CLASS. Relationships can be formally described In an ordered pair form Ternary Relationships Ternary relationship is the one that involves three entities e. G. STUDENT-CLASS-FACULTY N-ray Relationships relationships in data model are binary or at most ternary but we could define relationship set linking any number of entity sets I.

E. -ray relationship Many-to-many relationships A relationship that is multi-valued in both directions is a many-to-many relationship. An employee can work on more than one project, and a project can have more than one employee. The questions "What does Dolores Quintal work nor, and "Who works on project FIFO? " both yield multiple answers. A many-to-many relationship can be expressed in a table with a column for each entity ("employees" and "projects"), as shown in the following example.One-to-one relationships One-to-one relationships are single-valued in both directions.

A manager manages one department; a department has only one manager. The questions, "Who is the both have single answers. The relationship can be assigned to either the DEPARTMENT table or the EMPLOYEE table. Because all departments have managers, but not all employees are managers, it is most logical to add the manager to the DEPARTMENT table, as shown in the following example.

Integrity constraints are used to ensure accuracy and consistency of data in a relational database. Citation needed] Data integrity is handled in a relational database through the concept of referential integrity. There are many types of integrity constraints that play a role in referential integrity. Entity Integrity[edit] For more details on this topic, see Entity integrity.

The entity integrity constraint states that no primary key value can be null. This is because the primary key value is used to identify individual tepees in a relation. Having null value for the primary key implies that we cannot identify some pulps.This also specifies that there may not be any duplicate entries in primary key column key word Referential Integrity[edit] For more details on this topic, see Referential integrity. The referential integrity constraint is specified between two relations and is used to maintain the consistency among tepees in the two relations.

Informally, the referential integrity constraint states that a duple in one relation that refers to another relation must refer to an existing duple in that relation. It is a rule that maintain consistency among the rows of the two relations.Domain Integrity[edit] The domain integrity states that every element from a relation should respect the type and restrictions of its corresponding attribute. A type can have a variable length which needs to be respected. Restrictions could be the range of values that the element can have, the default value if none is provided, and if the element can be NULL.

User Defined Integrity[edit] A business rule is a statement that defines or constrains some aspect of the business. It is intended to assert business structure or to control or influence the behavior of the business.

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  • Entrepreneurship

Database Management System

Updated 14 June 2023

Subject Entrepreneurship ,  Space ,  Management

Downloads 28

Category Business ,  Science

Topic Business Analysis ,  Aerospace ,  Comparative Analysis

Company A is regarded as one of the leading firms in the aerospace industry. Being a merchant supplier and a contractor, Company A specializes in the production and delivery of complex aerospace systems such as aviation, space, and military to a global customer base. Propulsion systems, satellites and components and services, composite aerospace structures, missiles, and space launch vehicles are among our most successful products. The goal of this research project is to discover, communicate, evaluate, explore, and reflect on the logical and efficient application of management information systems in order to give a laborsaving, timesaving, and cost-effective means of doing business. Implementation of an efficient transaction processing system (TPS) is paramount for effective management of operations in almost every aspect of the company. In deliberation and examination of a TPS, we must consider our needs and important aspects of a TPS as a function of our business models. The best TPS for our use will be evaluated by performance, continuous availability, data integrity, simplify of use, and modular growth. The performance of the TPS will be measured regarding total transactions in a given period. Company A is a high volume, a multi-disciplinary company that requires real-time processing to handle the sheer volume of transactions. This is an efficient means of processing due to accurately updating data in real time (Turban, Volonino, Wood, P. 50).

Real-time processing is the most appropriate type of process in the purview of this businesses since it guarantees and appropriate response to a stimulus or request quickly to affect the condition that caused the stimulus (Schuster). An evaluation of continuous availability is required due to the importance of TPSs. An efficient TSP must be available at all times to ensure the business continues to operate as intended. An unexpected shutdown or lack of availability of the TPS will inhibit operations. An escrow system must be used to ensure the integrity of inventory for purchase. This means a sale must accurately hold a product as ‘pending’ and must not sell the same product to multiple end-users. A TPS must have an efficient and simple interface to minimize errors of data entry and allow for corrections. Expanding this system for scalability is a must since this company is poised growth and must work to eliminate the need for a complete replacement of the TPS for scalability issues (Information Resources Management Association, 2016). The overall TPS must perform and manage the following tasks which include but are not limited to: sales, production, inventory, purchasing, shipping, receiving, accounts payable, billing, accounts receivable, payroll, and a general ledger. Working to automate these processes as much as possible will undoubtedly result in giving A Company competitive advantage as well increasing optimal performance and increase operational efficiency.

Database Management System is an organizational system that enables proper organization of data in a database (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). DBMS in A company allows data storage and transformation to information to facilitate the decision-making process. Database Management System is made of three elements namely; physical database, database engine, and database scheme. Physical database is responsible for gathering all the files that have the data. Database engine makes the access and modification of contents of database possible (Schuster, 1981). Database scheme is the specifications of the logical structure of the stored data in a database. Considering the size of A and activities operated by the company, it is evident that database management system is required to keep and transform data for the sake of future analysis and decision making by the A company management.

Database management system is used in various departments within A Company. Marketing Department and Human Resource Departments are among the departments that utilize DBMS considering the data generated by the marketing and human resource (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). Evaluation regarding the use of DBMS in A Company focuses on Marketing Department because marketing is an important department of the organization because it is the department that involves external stakeholders such as customers and society. Data collected from the external environment is essential for A Company because it helps in storing and organizing data to a mode, in which it helps the company to analyze the comments, feedback, and surveys.

A Company handles and produces numerous aerospace activities thus it has a huge number of customers worldwide. Sales analysis is important because it can be simulated to forecast the future sales and market environment (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). The database helps the marketing department to arrange certain data regardless of the size for marketing managers to perform analysis essential for making both operational decisions and strategic decisions. The marketing data analysis puts A Company a competitive advantage due to the reason that the company is operated based on both internal and external market forces.

The production in A Company is based on the demand in the market. Demand prediction depending on seasons is possible when DBMS is well configured to capture and arrange sales data for every product, region and season (Information Resources Management Association, 2016). The A Company sales its products worldwide; hence regional sales are always taken into consideration to ensure distribution of products is made based on the demand of every production per region. The aerospace industry has fewer players in the market, but quality production in aerospace is of the essence. Therefore, DBMS plays an essential role in storing, arranging and analyzing sales data.

A Company is a company characterized by specific features regarding processes. Requirements specifications are essential to keep the company operations smooth and reduce major restructuring in departments. Vendors offering data storage and analysis solution give options for customers to order DBMS as per client workflows (Schuster, 1981). Selecting a customized DBMS is a better solution for A Company because there are various types of DBMS namely; Hierarchical Database, Network Database, Relational database and Object-oriented database (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). The final DBMS architecture should be fitting the specific requirements of A Company; hence the project management team should provide the specification to the vendor before making an order of a particular DBMS. For the case of A Company, a recommended DBMS should be a customized Hierarchical database.

Our company is a multinational company, and we recommend network database due to the reason that we have branches and vendors across the world. Branch and vendors data can help our company to collect sufficient data that can enhance analysis and decisions regarding our products. Vendors’ data can help us understand feedback from the customers worldwide; thus survey can frequently be done to keep the company posted regarding the client's preferences (Information Resources Management Association, 2016).

Hieratical DBMS operates in a parent-child and tree-like model. The hierarchical database management system is designed in apparent child relationship because it has the parent database and small databases that are linked to the major database (parent). For example, our company database can be organized in order such as headquarters, followed by branch, department, teams and teams and team members considering that in within our department we have a team assigned to particular duties. Team members have tasks assigned, and they are supposed to report to the team, where the team can integrate all the reports to the respective department, and the department can be able to gather all the information then post the branch, where the branch will update the database and finally send to the headquarters (Information Resources Management Association, 2016). Navigation, local parent pointers and one-to-many and redundancy are the solution framework that Hierarchical DBMS can offer to our company.

Navigation is easy and particular when using the Hierarchical model because the predetermined structures direct the access paths in the hierarchical model (Information Resources Management Association, 2016). A Company has many branches and departments; thus Hierarchical model can provide a solution regarding accessing specific records, query moves right from the segment in the database down via the branches. It will be fine for A Company because the experts in the company are knowledgeable thus tracing the location of the files. It reduces the redundancy because files are arranged in the records based on the categories and codes thus it reduces the duplication of files in headquarters and branches. Our company has many ongoing projects thus the pointers can help in separating the projects database where the project information is stored.

The strengths of the Hierarchical can be realized from the results. The hierarchical database enables the company to organize its employees in an orderly manner that can give the human resource department easy task in tracing performances per employee and departmentally. Isolation of the sections is enhanced by the hierarchical database thus increasing the lack of collaboration among the employees due to the reason that hierarchical database (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). Another weakness is that it makes the departments to prioritize their goals instead of the A Company goals and objectives. The cost of a hierarchical database model is high regarding acquisition and maintenance because it requires expensive hardware, software and the high skills to maintain and update the system due operation.

The hierarchical database can improve the company performances due to the reason that it streamlines the storage and transformation of data helping the strategic management to make both long-term and short-term decision helping the company to win large market share. Technology advancement helps the company to produce high-quality products; hence making A Company gain competitive advantage. Information systems are the source of efficiency because it reduces redundancy of activities and makes work consistent (Rocha, Correia, Adeli, Reis, & Costanzo, 2017).

Customer Relationship Management

A customer relationship management is a term that the management of companies uses to set strategies, practices, and implementation of technologies that is aimed at managing and analyzing customer relations to the company. The main objective of a customer relationship management is to improve the relationship between customers and the company. A well customer relationship retains customers and drives sales growth. The relationship between customers and the company is improved by a well-designed website, direct telephone lines that are responded to immediately, direct emails with instant responses, and advertisements through the social media platform (Kumar, & Reinartz, 2012).

A customer relationship management is a term that the management of companies uses to set strategies, practices, and implementation of technologies that is aimed at managing and analyzing customer relations to the company. The main objective of a customer relationship management is to improve the relationship between customers and the company. A good customer relationship retains customers and drives sales growth. The relationship between customers and the company is improved by a well-designed website, direct telephone lines that are responded to immediately, direct emails with instant responses, and advertisements through the social media platform (Kumar, & Reinartz, 2012).

Customer relationship management improves customer service. In a well-managed customer relationship management, customer queries are quick, easily, effectively, and efficiently responded to promptly. For example, it will be easier for a company to track customer complaints to determine why the company is performing poorly.

A customer relation sets a sales strategy through an evaluation of a company’s journal over, say a five-year period. The sales trend will give a guide to the type of customers to target to generate more revenue. Apart from a customer relationship management giving a sales trend, the relationship helps accountants to track the profitability of the company through the customer records (Stolt, 2010). The amount of revenue lists every client, and the shipping costs are considered to avoid earning a low-profit margin. A customer relationship management can be a tool for more visibility into the base of the clients that will gather tactics on how to get long-term profitability from the customers.

Customer relationship management leads to a success of business by organizing data in databases where each party can easily access the information at any time. A well-organized data system helps the marketing team to advertise and sell a company’s products with ease because the required information is organized in one database. As a result, the sales of an entity grow leading to a yield of high profits by a company.

Flow of Information in a CRM System

Finalize Order

Confirm With Stock

Received Order

Order Shipment

Create Invoice

Many apps and software are in the market that can be used by A Company in the management of sales. The relationship between customers and A Company can be regulated by the use of a well-defined and easy to use customer relationship management. However, the best Customer relationship management that can be recommended for A Company is either NetSuite CRM+ or Nutshell.

NetSuite CRM+

NetSuite CRM+ is a fully designed front-office customer relationship management solution, which integrates all the customer needs of the company and includes an accounting system. The accounting system is majorly for generating financial reports. Every sale in the system is recorded and customized reports generated whenever the need arises. Apart from the operations of accounting offered by the NetSuite CRM+ software, the e-commerce, shipping, and warehousing applications are supported by the system to aid the whole process of sales. Work performance of department can be done collaboratively to enhance better customer service, increased customer satisfaction, and repeat business and retention rates maintained.

Small businesses are recommended to use Nutshell customer relationship management because it is simple to use and creates solutions for many of the challenges faced by other customer relationship management. Nutshell motivates the sales executive team in embracing the products. The Nutshell is an award-winning customer relationship management that enables the performance of important things quick and an intuitive collaboration and sales process that integrates emailing and out-f-the-box mode of reporting to the management. The emails can be easily synched with Google Apps or Gmail, Outlook or Office, and more other user-friendly interface packages that are affordable in the market. The Nutshell Customer relationship management app can serve well A Company in the sale of aerospace items due to many customers communicating and placing orders via email.

The integration of customer relationship management by the A Company will improve the effectiveness and efficiency of operations in the sales department and the finance section. The use of a customer relationship management motivates employees due to the generation of reports in less time compared to the manual process. When the time spent in the analysis of reports can be a major contributor to delays and late managerial decisions. But, with the use of the Customer relationship management apps in A Company, the sales team and the finance team can prepare and forecast for future sales in less time that can allow other activities to take place. The increased efficiency and steady flow of events in the company will enhance high productivity and increased revenue due to the forecasted sales that are used to set target sales within a financial period.

New systems and applications in the company require orientation and lessons on how to operate and maintain. The need or training and orientation are a major challenge to the staff, as it will need time. The weakness of the new system is the time spent in the training as well as the resources invested in the learning process for employees to be acquainted with the system. However, the extra cost incurred in the installation of the system is transferred to the customers. The setting of product prices and the determination of variable and fixed costs are rested in the management team. When cost analysis is done, the sales volume is determined and ascertained whether it meets the profit target of a company. Despite the need to earn a profit after the transactions are complete, the system of determining the cost is majorly based on the sales volume. When analyzing the cost of items using the cost-volume-profit method, the selling price per unit, variable costs per unit and fixed costs are assumed constant all through. The exercise of ascertaining costs before setting the price on products helps in determining the markup to include the total cost to reach the set profit target.

In the determination of cost, the A Company should not consider a cheap customer relationship management but get a system that can help in solving the challenges faced by the company. Instead of getting a less expensive customer relationship management that will work for a short duration and require re-installation or phase out, the management will need to check the compatibility of the two suggested customer relationship management software and choose wisely.

Computer Integrated Manufacturing

A Company manufactures aerospace products such as the propulsion systems, satellites and components and services, composite aerospace structures, missiles, and space launch vehicles. Considering the types of the products produced by A Company, it is clear that the company needs computerized production processes to avoid manual errors. Manual errors are normal, but with the application Computer Integrated Manufacturing system, errors can be eliminated because every product is designed, prototyped and tested before being executed to the market (Information Resources Management Association, 2016). Computer Integrated Manufacturing system works can work well in A Company because it allows employee’s processes to exchange information with one another and initiate actions.

In manufacturing department Computer Integrated Manufacturing system enhances the computerized designing of aerospace products in three dimensions (3D) (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). Manufacturing is done after proper simulation to avoid errors and wastage. It integrates total enterprise manufacturing via the application of the integrated systems and the data communication in with the help of the management philosophies which advances the personal and organizational efficiency. Tedious problems are solved by the Computer Integrated Manufacturing system because it is capable of controlling all the A Company production processes.

Vendors offering the Computer Integrated Manufacturing system services invite the client customized requirements to make sure all the company problems are solved in a specific approach to avoid problems that are brought by the purchase and installation of the universal CIM (Information Resources Management Association, 2016). Customized Computer Integrated Manufacturing System is beneficial to manufacturing department because it reduces the design cost by 15%-30%, cuts the in-shop time of the part by 30%-60%, it increases the production in the manufacturing department by 40%-70% and improves the quality of the aerospace products produced by A Company and reduces the scrap by 20%-50%.

Solutions provided by Computer Integrated Manufacturing System because it has three distinguishing factors from the conventional manufacturing methods (Information Resources Management Association, 2016). CIM is capable of storing data, retrieving, and manipulating and presentation data based on the predetermined form designed by the A Company experts. It has the mechanism of sensing the state and modifying the process to fit the expectations. It has the algorithms for integrating the data processing component with the modification component. Therefore, CIM is an example of the information and communication technologies in manufacturing department of A Company.

The weakness of the CIM is that it increases the data integrity issues in the company because the higher the automation of the activities in the manufacturing department, the more critical cab be the integrity of the A Company data applied to control the machines in the manufacturing department. Without robots, the CIM finished products need extra human labor ensure the good safeguard for data signals that are applied to control the machines in the manufacturing department. Competent engineers are needed to handle the situations that were not forecasted by the designers of the CIM (Information Resources Management Association, 2016).

Supply Chain Manufacturing

Supply Chain manufacturing aid A Company to integrate its business strategy with supply chain initiatives to propel company towards excellence. Supply Chain Manufacturing encompasses new product development, integrated demand planning, inventory strategy, manufacturing footprint strategy, logistic optimization, sustainability and sourcing and commodity management (Information Resources Management Association, 2016). In A Company we embrace problematic methods and analytic capabilities to lower costs and increase productivity.

During the selection of an ideal Supply Chain manufacturing system, a company expert provides a checklist to ensure the vendor can deliver a system with all the specific requirements of the company to avoid errors and inconvenience during the ordering, manufacturing and supplying the products to the client worldwide. Aerospace products are very delicate products thus errors in production are not supposed to be tolerated in all circumstances because any simple errors can cause huge losses (Rocha, Correia, Adeli, Reis, & Costanzo, 2017).

Supply Chain manufacturing should be characterized by accuracy, timeliness, simplicity, and appropriates. A Company is a system made of other subsystems such as the employees thus employees in the manufacturing department are supposed to be updated regarding the performances of the Supply Chain manufacturing to avoid losses (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). External stakeholder such as the customers and suppliers are supposed to be considered when advancing the supply chain in the manufacturing department. Therefore, the scalability of the Supply Chain manufacturing should take into consideration all the stakeholders and key objectives of A Company in the aerospace production sector.

The benefits of Supply Chain Manufacturing are endless in A Company because it boosts the morale of the employees in the manufacturing department and the entire company. Supply Chain Manufacturing offers the financial benefits to the company because it contributes to the improvement of the brands’ reputation both with the A Company and the aerospace products’ consumers, partners, and the financial backers.

Supply Chain Manufacturing can help A Company to decrease production cost because it manufacturing department will be able to link the supply and the manufacturing thus production will be based on the demand and supply. The material shortage can be minimized by the Supply Chain Manufacturing because all the products manufactured can be supplied to the clients without excess holding cost (Rocha, Correia, Adeli, Reis, & Costanzo, 2017).

Supply Chain Manufacturing improves the financial position because it helps to control and decrease the supply chain costs. Employment of Supply Chain Manufacturing in A Company can dramatically increase the company’s profit (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). Fixed assets cost can decrease due to the reason that it reduces the number of plants, machines, warehouses and the transporting vehicles in the supply chain. The vendors can be advised by the A Company experts to customize the Supply Chain Manufacturing system to set up a network to serve a Company cost-effectively and efficiently. Supply Chain Manufacturing can boost the cash flow in A Company because it decreases the manufacturing processes and increases the supply of the products in the market. The invoicing of the customers can be made sooner because of the reduction in the delivery period. Therefore, due to the benefits and solutions provided by Supply Chain Manufacturing, it is clear that A Company can keep its competitive advantage in the market.

Installation Supply Chain Manufacturing in A company can cause reluctance and lack of creativity among the employees in the manufacturing and procurement department due to the automation of services (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). The cost of maintaining Supply Chain Manufacturing is relatively high as compared to the cost of maintaining the convention supply chain as far as the cost hiring experts, acquiring hardware and software and operational cost is concerned.

In conclusion, information systems are sources of effectiveness and efficiency. Proper designing of the information system in A Company is termed as profitability because the costs of acquiring and maintaining the systems are less as compared to the benefits or profits earned from the information systems. It is the duty of the company experts to provide the specification of all the information systems needed in the company to keep competitive advantage to the company as far as the production of aerospace products are concerned.


Schuster, S. (1981). “In Depth, Relational Data Base Management.” Computer World. Retrieved April 23, 2017

Turban, E. Volonino, L.& Wood, G R. (2015). Information Technology for Management: Digital Strategies for Insight, Action, and Sustainable Performance. (10e) Wiley

ISBN: 978- 111889778-2

Rocha, A., Correia, A. M., Adeli, H., Reis, L. P., & Costanzo, S. (2017). Recent Advances in Information Systems and Technologies: Volume 3. Cham: Springer.

Information Resources Management Association. (2016). Big data: Concepts, methodologies, tools, and applications. Hershey, PA: Information Science Reference.

Kumar, V., & Reinartz, W. (2012). Customer Relationship Management: Concept, Strategy, and Tools. Berlin, Heidelberg: Springer Berlin Heidelberg.

Stolt, R. (2010). The Importance of Customer Relationship Management in Business Marketing. München: GRIN Verlag GmbH.

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Essays on Database Management System

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A database forms a significant constituent of the information system, particularly in business-oriented systems. As a consequence, the process of developing database system is a component of system development. The division of the development process into a collection of steps or phases is a core feature of software engineering. Each of the phases focuses on a single aspect of the development. Development life cycle is the process software, or a database system undergoes from planning to its implementation. It is imperative to check each phase or step for correctness before moving to the next phase (Bestavros, Lin & Son, 2012).

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The Coolest Database System Companies Of The 2024 Big Data 100

Part 2 of CRN’s Big Data 100 takes a look at the vendors solution providers should know in the database systems space.

essay on database system

Running The Bases

By 2025 the total amount of digital data generated, gathered, copied and consumed is expected to be in the range of 175 to 180 zettabytes. And more of that data is spread across distributed, hybrid-cloud and multi-cloud networks.

To make productive use of the ever-growing volumes of data, businesses and organizations need the right database systems to manage all that data and make it available for transactional and analytical applications.

Not surprisingly, the global market for database management systems, which reached $63.50 billion in 2022, is forecast to grow at a CAGR of 11.56 percent to $152.36 billion by 2030, according to Zion Market Research .

As part of the CRN 2024 Big Data 100 , we’ve put together the following list of database system companies—from well-established vendors to those in startup mode—that solution providers should be familiar with.

These vendors offer next-generation relational database systems that can handle growing volumes of data and transactions, analytical databases designed to process complex queries against huge data sets – and some databases that can do both – along with more specialized systems such as graph databases and time series databases.

This week CRN is running the Big Data 100 list in a series of slide shows, organized by technology category, spotlighting vendors of business analytics software, database systems, data warehouse and data lake systems, data management and integration software, data observability tools, and big data systems and cloud platforms.

Some vendors have big data product portfolios that span multiple technology categories. They appear in the slideshow for the technology segment in which they are most prominent.

essay on database system

Top Executive: CEO Subbu Iyer

Aerospike markets its next-generation NoSQL database for high performance transactional, analytical and AI/machine learning tasks. It describes its database product as a “massively scalable, millisecond latency, real-time database” that provides fast reads/writes and “unmatched uptime.”

Aerospike’s offerings include the Aerospike Database 7 server (introduced in November 2023), a cloud database-as-a-service, and a cloud managed service on AWS, Azure or Google Cloud. Also available is a graph database for managing real-time data relationships and vector search capabilities – the latter for powering AI applications.

Earlier this month Aerospike, headquartered in Mountain View, Calif., raised an impressive $109 million in a growth capital funding round.

essay on database system

Cockroach Labs

Top Executive: CEO Spencer Kimball

Cockroach Labs develops CockroachDB, a high-availability, distributed SQL database for developing and running mission-critical, transaction-heavy applications.

The New York-based company says its database is built on top of a transactional and consistent key-value store that is highly scalable and can withstand datacenter failures. (The company’s name is inspired by the insect, as is the CockroachDB motto: “Scale fast. Survive disaster. Thrive everywhere.”)

Earlier this month Cockroach was selected to join the Google Distributed Cloud, which does not require Google Cloud connectivity of the public internet to manage infrastructure, services, APIs or tooling. Google Distributed Cloud is targeted toward the public sector and highly regulated industries that require flexibility to support regional data residency, security or isolation regulations.

essay on database system

Top Executive: President and CEO Matt Cain

Couchbase develops a NoSQL cloud database for business-critical and AI-powered applications. The core technology is a distributed, JSON document database “with all the desired capabilities of a relational DBMS,” according to the company.

In addition to the NoSQL database server, the company offers its Couchbase Capella database-as-a-service for GenAI, vector search and mobile application services.

For its fiscal 2024 (ended Jan. 31) Santa Clara, Calif.-based Couchbase reported revenue of $180.0 million, up 16 percent from fiscal 2023.

essay on database system

Top Executive: CEO Chet Kapoor

DataStax develops its DataStax Enterprise NoSQL and vector database on the open-source Apache Cassandra database with a focus on providing real-time data for applications. The flagship database, in turn, is the foundation for Astra DB, the company’s cloud-native database-as-a-service offering.

The Santa Clara, Calif.-based company also markets its Astra Streaming data streaming software for real-time data and generative AI applications.

In a move to strengthen its hand in the AI data and application development arena, DataStax earlier this month acquired Langflow , provider of a popular framework for building generative AI applications that use retrieval augmented generation (RAG) technology.

essay on database system

Top Executive: CEO Mike Waas

Datometry calls itself “a pioneer” in developing database system virtualization technology.

Database virtualization decouples the database layer, usually residing between data storage and applications within an application stack, making it possible to pool compute and storage resources and allocate them as needed, according to the company. The technology prevents vendor lock-in, according to the company, and makes it easier to move to migrate from one database to another.

Datometry, headquartered in San Francisco, provides editions of its Hyper-Q platform for Microsoft Azure Databricks, Microsoft Azure Synapse, Google BigQuery and Amazon Red shift.

In November the company debuted OpenDB, a fully compatible drop-in replacement for Oracle databases. (OpenDB v2 shipped in February.) In March it announced support for Microsoft Fabric and earlier this month announced support for Oracle workloads on native Google databases.

essay on database system

Top Executive: CEO Kevin Dallas

EDB (previously EnterpriseDB) offers secure, scalable database software based on the open-source Postgres SQL database. The company’s popular database is compatible with the Oracle relational database.

In addition to the EDB Enterprise Advanced database server and EDB Postgres Distributed database, the company provides the EDB BigAnimal Postgres-as-a-service managed database.

Dallas took over as CEO of Bedford, Mass.-based EDB in August 2023 and in a recent interview with CRN outlined his plans to expand the use cases for the EDB database portfolio beyond transaction processing into data analytics and AI applications.

In October 2023 EDB acquired Splitgraph, which develops a Postgres-compatible serverless SQL API for building data-driven applications that support hundreds of data sources.

essay on database system

Top Executive: CEO Yury Selivanov

EdgeDB describes its offering as “an open-source database designed as a spiritual successor to SQL and the relational paradigm.”

The database is powered by the Postgres query engine with a data schema model that the company calls “graph-relational” and a query language, EdgeQL, that “blends the best” of GraphQL and SQL.

EdgeDB 1.0 launched in February 2022. The company raised $15 million in a Series A funding round in November 2022.

In November 2023 the San Francisco-based company debuted EdgeDB 4.0 and a cloud edition of the database.

essay on database system

Top Executive: CEO Joerg Tewes

Exasol develops a column-oriented, in-memory database for high-performance data analytics and business intelligence tasks. The database works with a long list of the most popular analytics tools including Tableau, Qlik Sense, MicroStrategy, Looker, PowerBI, Yellowfin and more.

In February of this year Exasol launched Espresso AI – built on the company’s Exasol Espresso query engine – which combines business intelligence and AI capabilities to improve BI reports with predictive machine learning models for such tasks as demand forecasting, fraud detection and churn prediction.

Tewes was named Exasol’s CEO effective Jan. 1, 2023, taking over from the previous CEO, Aaron Auld, who stepped down from the post in September 2022. Exasol is headquartered in Nuremberg, Germany.

essay on database system

Top Executive: CEO Fangjin Yang

Imply provides a real-time analytics platform based on Apache Druid, an open-source analytics database that was originally developed by Imply’s founders.

Imply, based in Burlingame, Calif., says analytics applications built on Imply Polaris, the company’s fully managed cloud database-as-a-service, can scale up to any number of users, work with streaming or batch data, and deliver sub-second queries on millions – even trillions – of rows of data.

essay on database system

Top Executive: CEO Evan Kaplan

InfluxData develops the InfluxDB time series database for ingesting, analyzing and storing time-based columnar data. InfluxDB is the foundation for a number of InfluxData products including InfluxDB Cloud Serverless, InfluxDB Cloud Dedicated and InfluxDB Clustered.

In April 2023 the San Francisco-based company released InfluxDB 3.0 with a rebuilt database and storage engine that provides expanded time series capabilities across the company’s product portfolio.

essay on database system

Top Executive: CEO Nima Negahban

Kinetica develops a distributed, memory-first OLAP database that is designed to leverage GPUs and modern vector processors to boost the performance of complex queries across large volumes of real-time data, including temporal and geospatial data.

Over the last year Kinetica, based in Arlington, Va., has been incorporating AI and generative AI technology in its database to expand its natural language ad hoc querying capabilities, including adding ChatGPT in May for “conversational querying” analysis and then in September embedding a native large language model for running language-to-SQL analytics with enhanced privacy and security and greater fine-tuning capabilities.

essay on database system

Top Executive: CEO Paul O’Brien

MariaDB plc markets its namesake Enterprise Server relational database and the cloud-based MariaDB Managed Database, both based on the open-source MariaDB database.

While MariaDB plc’s database products are popular, the company (as distinguished from the MariaDB Foundation that manages the open-source MariaDB database itself) has struggled financially over the last year after going public in 2022. The company, with dual headquarters in Redwood City, Calif., and Dublin, Ireland, worked through two rounds of employee layoffs in 2023, issued a “going concern” warning in April, instituted a restructuring plan in October, and stopped selling products it considered non-core including SkySQL (spun off as a separate business) and Xpand.

In recent months MariaDB plc disclosed that it has received informal acquisition offers from K1 Investment Management and Progress Software Corp.

essay on database system

Top Executive: President and CEO Dev Ittycheria

MongoDB is one of the most successful of the new generation of database companies with its namesake document-oriented database and MongoDB Atlas, the company’s integrated suite of data services centered around a cloud database.

MongoDB’s focus is on providing developers with a platform for building data-centric and AI applications. The company’s database supports a broad range of tasks and data types including transactions, AI, edge computing, vector search, full-text search, operational data, streaming data, time-series data, geospatial data and graph data.

A major factor in MongoDB’s growth is the extensive strategic alliances the company has established with Amazon Web Services , Microsoft Azure , Google Cloud and Alibaba .

Revenue in New York-based MongoDB’s fiscal 2024 (ended Jan. 31) grew 31 percent year over year to $1.68 billion.

essay on database system

Top Executive: CEO Emil Eifrem

Neo4j offers a graph database that stores data using nodes, edges and properties – rather than traditional tables or documents – and emphasizes the relationships between different data entities.

Neo4j AuraDB is the company’s fully managed graph database-as-a-service and Neo4j Graph Data Science is the company’s analytics and machine learning system.

In March Neo4j announce a collaboration with Microsoft to develop a unified data system, combining Neo4j’s graph capabilities natively integrated with Microsoft Fabric and Microsoft Azure OpenAI service, that combines structured and unstructured data to meet the growing data needs of generative AI applications.

essay on database system

Top Executive: Rowan Trollope

Redis (previously Redis Labs) develops the Redis in-memory NoSQL database and markets commercial versions of the database and related software and a fully managed cloud database service. The high-performance Redis database is most popular for developing real-time data applications.

Redis, headquartered in Mountain View, Calif., also sponsors the source-available Redis database under the Redis Source Available License and Server Side Public License, a change from its previous availability under the open-source BSD license.

On March 21 Redis said it had acquired Speedb, which develops a data storage engine used to accelerate application performance across data caching and real-time use cases.

essay on database system

Top Executive: CEO Dor Laor

ScyllaDB develops the distributed, NoSQL database of the same name that’s designed for high-performance, data -intensive applications. The database is available in enterprise, cloud and open-source editions.

ScyllaDB is compatible with the open-source Apache Cassandra database and positions itself as a competitor to other NoSQL databases including MongoDB and Amazon DynamoDB. The ScyllaDB Enterprise 2024 1.0 release (unveiled Feb. 20) provided up to 50 percent higher throughput, 35 percent greater efficiency and 33 percent lower latency than the previous enterprise release, according to the company.

In October 2023 ScyllaDB, with headquarters in Sunnyvale, Calif., and Herzliya, Israel, raised $43 million in a new funding round led by Eight Roads Ventures.

essay on database system


Top Executive: CEO Raj Verma

SingleStore develops SingleStoreDB, a distributed, relational SQL database that supports both high-speed transactional and analytical workloads. SingleStore Helios is the company’s fully managed cloud database service.

SingleStore Pro Max, launched in January, is the company’s data platform for developing and running real-time AI applications. It offers such features as indexed vector search, an on-demand compute service for GPUs and CPUs, new change data capture capabilities for data ingest and egress, and a new free shared tier.

essay on database system

Top Executive: CEO Bala Kuchibhotla

Tessell’s database-as-a-service platform is used to set up, manage, secure and scale relational databases in the cloud including Oracle Database, Microsoft SQL Server, MySQL, PostgreSQL, Milvus and MongoDB.

Tessell says its platform, running on AWS and Microsoft Azure, can accelerate the migration of database workloads to cloud platforms and provide a unified control plane for managing databases across multiple cloud systems.

In January the San Ramon, Calif.-based company unveiled Tessell Database Lifecycle Management for Exadata@Azure, the latter the Oracle-Microsoft collaboration that makes the Oracle Database and Exadata services available directly within the Azure platform.

essay on database system

Top Executive: CEO Hamid Azzawe

TigerGraph provides the TigerGraphDB graph database and graph analytics software, along with the TigerGraph Cloud graph database-as-a-service and GraphStudio graph analytics user interface.

Because graph databases show the relationships between data entities, they are particularly well-suited for such tasks as product and service marketing, customer 360 management, recommendation engines, fraud detection, anti-money laundering, and risk assessment and monitoring.

The most recent database release from the Redwood City, Calif.-based company introduced extended support for workload management, real-time data ingestion monitoring, Kubernetes and OpenCypher.

essay on database system

Top Executive: CEO Bill Cook

The YugabyteDB high-performance, distributed SQL database is designed for running mission-critical, cloud-native transactional applications. Yugabyte Managed is the company’s fully managed database-as-a-service.

The Sunnyvale, Calif.-based company also offers YugabyteDB Voyager for migrating data from other databases, such as Oracle, PostgreSQL and MySQL, to Yugabyte.

Yugabyte was founded in 2016 by three former Facebook engineers. The company’s name includes “yuga,” the Sanskrit word for “era” or long period of time.

NASA’s Voyager 1 Resumes Sending Engineering Updates to Earth


NASA’s Voyager 1 spacecraft is depicted in this artist’s concept traveling through interstellar space, or the space between stars, which it entered in 2012.

After some inventive sleuthing, the mission team can — for the first time in five months — check the health and status of the most distant human-made object in existence.

For the first time since November , NASA’s Voyager 1 spacecraft is returning usable data about the health and status of its onboard engineering systems. The next step is to enable the spacecraft to begin returning science data again. The probe and its twin, Voyager 2, are the only spacecraft to ever fly in interstellar space (the space between stars).

Voyager 1 stopped sending readable science and engineering data back to Earth on Nov. 14, 2023, even though mission controllers could tell the spacecraft was still receiving their commands and otherwise operating normally. In March, the Voyager engineering team at NASA’s Jet Propulsion Laboratory in Southern California confirmed that the issue was tied to one of the spacecraft’s three onboard computers, called the flight data subsystem (FDS). The FDS is responsible for packaging the science and engineering data before it’s sent to Earth.

After receiving data about the health and status of Voyager 1 for the first time in five months, members of the Voyager flight team celebrate in a conference room at NASA’s Jet Propulsion Laboratory on April 20.

After receiving data about the health and status of Voyager 1 for the first time in five months, members of the Voyager flight team celebrate in a conference room at NASA’s Jet Propulsion Laboratory on April 20.

The team discovered that a single chip responsible for storing a portion of the FDS memory — including some of the FDS computer’s software code — isn’t working. The loss of that code rendered the science and engineering data unusable. Unable to repair the chip, the team decided to place the affected code elsewhere in the FDS memory. But no single location is large enough to hold the section of code in its entirety.

So they devised a plan to divide the affected code into sections and store those sections in different places in the FDS. To make this plan work, they also needed to adjust those code sections to ensure, for example, that they all still function as a whole. Any references to the location of that code in other parts of the FDS memory needed to be updated as well.

The team started by singling out the code responsible for packaging the spacecraft’s engineering data. They sent it to its new location in the FDS memory on April 18. A radio signal takes about 22 ½ hours to reach Voyager 1, which is over 15 billion miles (24 billion kilometers) from Earth, and another 22 ½ hours for a signal to come back to Earth. When the mission flight team heard back from the spacecraft on April 20, they saw that the modification worked: For the first time in five months, they have been able to check the health and status of the spacecraft.

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During the coming weeks, the team will relocate and adjust the other affected portions of the FDS software. These include the portions that will start returning science data.

Voyager 2 continues to operate normally. Launched over 46 years ago , the twin Voyager spacecraft are the longest-running and most distant spacecraft in history. Before the start of their interstellar exploration, both probes flew by Saturn and Jupiter, and Voyager 2 flew by Uranus and Neptune.

Caltech in Pasadena, California, manages JPL for NASA.

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Guest Essay

Liz Cheney: The Supreme Court Should Rule Swiftly on Trump’s Immunity Claim

A black-and-white photo of the U.S. Supreme Court building, with trees in the foreground.

By Liz Cheney

Ms. Cheney, a Republican, is a former U.S. representative from Wyoming and was vice chairwoman of the Jan. 6 select committee in the House of Representatives.

On Thursday, the U.S. Supreme Court will hear Donald Trump’s arguments that he is immune from prosecution for his efforts to steal the 2020 presidential election. It is likely that all — or nearly all — of the justices will agree that a former president who attempted to seize power and remain in office illegally can be prosecuted. I suspect that some justices may also wish to clarify whether doctrines of presidential immunity might apply in other contexts — for example, to a president’s actions as commander in chief during a time of war. But the justices should also recognize the profoundly negative impact they may have if the court does not resolve these issues quickly and decisively.

If delay prevents this Trump case from being tried this year, the public may never hear critical and historic evidence developed before the grand jury, and our system may never hold the man most responsible for Jan. 6 to account.

The Jan. 6 House select committee’s hearings and final report in 2022 relied on testimony given by dozens of Republicans — including many who worked closely with Mr. Trump in the White House, in his Justice Department and on his 2020 presidential campaign. The special counsel Jack Smith’s election-related indictment of Mr. Trump relies on many of the same firsthand witnesses. Although the special counsel reached a number of the same conclusions as the select committee, the indictment is predicated on a separate and independent investigation. Evidence was developed and presented to a grand jury sitting in Washington, D.C.

The indictment and public reporting suggest that the special counsel was able to obtain key evidence our committee did not have. For example, it appears that the grand jury received evidence from witnesses such as Mark Meadows, a former Trump chief of staff, and Dan Scavino, a former Trump aide, both of whom refused to testify in our investigation. Public reporting also suggests that members of Mr. Trump’s Office of White House Counsel and other White House aides testified in full, without any limitations based on executive privilege, as did Vice President Mike Pence and his counsel.

The special counsel’s indictment lays out Mr. Trump’s detailed plan to overturn the 2020 election, including the corrupt use of fraudulent slates of electors in several states. According to the indictment, senior advisers in the White House, Justice Department and elsewhere repeatedly warned that Mr. Trump’s claims of election fraud were false and that his plans for Jan. 6 were illegal. Mr. Trump chose to ignore those warnings. (Remember what the White House lawyer Eric Herschmann told Mr. Trump’s alleged co-conspirator John Eastman on Jan. 7, 2021: “Get a great f’ing criminal defense lawyer. You’re gonna need it.”) There is little doubt that Mr. Trump’s closest advisers also gave the federal grand jury minute-to-minute accounts of his malicious conduct on Jan. 6, describing how they repeatedly begged the president to instruct the violent rioters to leave our Capitol and how Mr. Trump refused for several hours to do so as he watched the attack on television. This historic testimony about a former president’s conduct is likely to remain secret until the special counsel presents his case at trial.

As a criminal defendant, Mr. Trump has long had access to federal grand jury material relating to his Jan. 6 indictment and to all the testimony obtained by our select committee. He knows what all these witnesses have said under oath and understands the risks he faces at trial. That’s why he is doing everything possible to try to delay his Jan. 6 federal criminal trial until after the November election. If the trial is delayed past this fall and Mr. Trump wins re-election, he will surely fire the special counsel, order his Justice Department to drop all Jan. 6 cases and try to prevent key grand jury testimony from ever seeing the light of day.

I know how Mr. Trump’s delay tactics work. Our committee had to spend months litigating his privilege claims (in Trump v. Thompson) before we could gain access to White House records. Court records and public reporting suggest that the special counsel also invested considerable time defeating Mr. Trump’s claims of executive privilege, which were aimed at preventing key evidence from reaching the grand jury. All of this evidence should be presented in open court, so that the public can fully assess what Mr. Trump did on Jan. 6 and what a man capable of that type of depravity could do if again handed the awesome power of the presidency.

Early this year, a federal appeals court took less than a month after oral argument to issue its lengthy opinion on immunity. History shows that the Supreme Court can act just as quickly , when necessary. And the court should fashion its decision in a way that does not lead to further time-consuming appeals on presidential immunity. It cannot be that a president of the United States can attempt to steal an election and seize power but our justice system is incapable of bringing him to trial before the next election four years later.

Mr. Trump believes he can threaten and intimidate judges and their families , assert baseless legal defenses and thereby avoid accountability altogether. Through this conduct, he seeks to break our institutions. If Mr. Trump’s tactics prevent his Jan. 6 trial from proceeding in the ordinary course, he will also have succeeded in concealing critical evidence from the American people — evidence demonstrating his disregard for the rule of law, his cruelty on Jan. 6 and the deep flaws in character that make him unfit to serve as president. The Supreme Court should understand this reality and conclude without delay that no immunity applies here.

Liz Cheney, a Republican, is a former U.S. representative from Wyoming and was vice chairwoman of the Jan. 6 select committee in the House of Representatives.

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This paper is in the following e-collection/theme issue:

Published on 22.4.2024 in Vol 26 (2024)

Patient and Staff Experience of Remote Patient Monitoring—What to Measure and How: Systematic Review

Authors of this article:

Author Orcid Image

  • Valeria Pannunzio 1 , PhD   ; 
  • Hosana Cristina Morales Ornelas 2 , MSc   ; 
  • Pema Gurung 3 , MSc   ; 
  • Robert van Kooten 4 , MD, PhD   ; 
  • Dirk Snelders 1 , PhD   ; 
  • Hendrikus van Os 5 , MD, PhD   ; 
  • Michel Wouters 6 , MD, PhD   ; 
  • Rob Tollenaar 4 , MD, PhD   ; 
  • Douwe Atsma 7 , MD, PhD   ; 
  • Maaike Kleinsmann 1 , PhD  

1 Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands

2 Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands

3 Walaeus Library, Leiden University Medical Center, Leiden, Netherlands

4 Department of Surgery, Leiden University Medical Center, Leiden, Netherlands

5 National eHealth Living Lab, Department of Public Health & Primary Care, Leiden University Medical Center, Leiden, Netherlands

6 Department of Surgery, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, Netherlands

7 Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands

Corresponding Author:

Valeria Pannunzio, PhD

Department of Design, Organisation and Strategy

Faculty of Industrial Design Engineering

Delft University of Technology

Landbergstraat 15

Delft, 2628 CE


Phone: 31 15 27 81460

Email: [email protected]

Background: Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience–measuring methods and tools exists.

Objective: This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain.

Methods: Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By “RPM interventions,” we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique.

Results: In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation.

Conclusions: At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.


Background and aim.

This is a scenario from the daily life of a patient:

A beeping sound, and a message appears on the smartphone screen: “Reminder: check glucose before bedtime.” Time to go to sleep, indeed, you think while putting down your book and reaching for the glucometer. As you wipe the drop of blood away, you make sure that both Bluetooth and Wi-Fi are on in your phone. Then, the reading is sent: you notice it seems to be rather far from your baseline. While you think of what you might have done differently, a slight agitation emerges: Is this why you feel so tired? The phone beeps again: “Your last glucose reading seems atypical. Could you please try again? Remember to follow these steps.” Groaning, you unwrap another alcohol wipe, rub your finger with it, and test again: this time, the results are normal.

Some patients will recognize certain aspects of this scenario, particularly the ones using a form of remote patient monitoring (RPM), sometimes referred to as remote patient management. RPM is a subset of digital health interventions that aim to improve patient care through digitally transmitted, health-related patient data [ 1 ]. Typically, RPM interventions include the use of 1 or more sensors (including monitoring devices, wearables, or implants), which collect patient data in or out of the hospital to be used for remote clinical decision-making. Partly due to a rapid expansion during the COVID-19 pandemic [ 2 - 5 ], the RPM domain has by now expanded to reach a broad range of medical specialties, sensing technologies, and clinical contexts [ 1 , 6 , 7 ].

RPM is presented as a strategy for enabling health care systems worldwide to face the pressing challenges posed by aging populations [ 8 - 10 ], including the dwindling availability of health care workers [ 11 ] and rising health care costs [ 12 ]. This is because deploying effective RPM solutions across health systems holds the potential to reduce health care resources use, while maintaining or improving care quality. However, evidence regarding RPM effectiveness at scale is mixed [ 13 ]. Few large-scale trials demonstrating a meaningful clinical impact of RPM have been conducted so far, and more research is urgently needed to clarify and address determinants of RPM effectiveness [ 7 ].

Among these determinants, we find the experience of patients and staff using RPM interventions. As noticeable in the introductory scenario, RPM introduces radical experiential changes compared to in-person care; patients might be asked to download and install software; pair, charge, and wear monitoring devices; submit personal data; or attend alerts or calls, all in the midst of everyday life contexts and activities. Similarly, clinical and especially nursing staff might be asked to carry out data analysis and administrative work and maintain remote contact with patients, often without a clear definition of roles and responsibilities and in addition to usual tasks [ 14 ].

Because of these changes, patient and staff experience constitutes a crucial aspect to consider when evaluating RPM interventions. Next to qualitative methods of experience evaluation, mixed and quantitative methods are fundamental, especially to capture information from large pools of users. However, the current RPM experience-measuring landscape suffers from a lack of methodological standardization, reflected in both what is measured and how it is measured. Regarding what is measured, it has been observed that a large number of constructs are used in the literature, often without a clear specification of their significance. This can be noticed even regarding popular constructs, such as satisfaction: Mair and Whitten [ 15 ], for instance, observe how the meaning of the satisfaction construct is seldom defined in patient surveys, leaving readers “unable to discern whether the participants said they were satisfied because telemedicine didn't kill them, or that it was ‘OK,’ or that it was a wonderful experience.” Previous work also registers a broad diversity in the instruments used to measure a specific construct. For instance, in their review of RPM interventions for heart failure, Kraai et al [ 16 ] report that none of the papers they examined used the same survey to measure patient satisfaction, and only 1 was assessed on validity and reliability.

In this proliferation of constructs and instruments, no comprehensive overview exists of their application to measuring patient and staff experience in the RPM domain. The lack of such an overview negatively affects research in this domain in at least 2 ways. At the level of primary research, RPM practitioners and researchers have little guidance on how to include experience measuring in their study designs. At the level of secondary research, the lack of consistently used measures makes it hard to compare results between different studies and RPM solutions. Altogether, the lack of standardization in experience measuring constitutes a research gap that needs to be bridged in order for RPM to fully deliver on its promises.

In this review, this gap is addressed through an effort to provide a structured overview of patient and staff experience constructs and instruments used in RPM evaluation. First, we position the role of RPM-related patient and staff experience within the broader system of care using the Quadruple Aim framework. Next, we describe the systematic review we performed of patient and staff experience–relevant constructs and instruments used in contemporary research aimed at evaluating RPM interventions. After presenting and discussing the results of this review, we propose a set of guidelines for RPM experience evaluators and indicate directions for further research.

The Role of Patient and Staff Experience in RPM

Many characterizations of patient and staff experience exist [ 17 - 19 ], some of which distinguish between determinants of experience and experience manifestations [ 20 ]. For our review, we maintained this distinction, as we aimed to focus on the broad spectrum of factors affecting and affected by patient and staff experience. To do so, we adopted the general conceptualization of patient and staff experience as characterized in the Quadruple Aim, a widely used framework for health system optimization centered around 4 overarching goals: improving the individual experience of care, improving the experience of providing care, improving the health of populations, and reducing the per capita cost of care [ 21 ]. Adopting a Quadruple Aim perspective allows health system researchers and innovators to recognize not only the importance of patient and staff experience in their own rights but also the inextricable relations of these 2 goals to the other dimensions of health system performance [ 22 ]. To clarify the nature of these relations in the RPM domain, we provide a schematic overview in Figure 1 .

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Next, we refer to the numbers in Figure 1 to touch upon prominent relationships between patient and staff experience in RPM within the Quadruple Aim framework and provide examples of experience constructs relevant to each relationship:

  • Numbers 1 and 2: The characteristics of specific RPM interventions directly affect the patient and staff experience. Examples of experience constructs related to this mechanism are expressed in terms of usability or wearability , which are attributes of systems or products contributing to the care experience of patients and the work experience of staff.
  • Numbers 3 and 4: Patient and staff experiences relate to each other through care delivery. Human connections, especially in the form of carer-patient relationships, represent a major factor in both patient and staff experience. An example of experience constructs related to this mechanism is expressed in terms of the quality of the relationship .
  • Numbers 5 and 6: A major determinant of patient experience is represented by the health outcomes achieved as a result of the received care. An example of a measure of quality related to this mechanism is expressed in terms of the quality of life , which is an attribute of patient experience directly affected by a patient’s health status. In contrast, patient experience itself is a determinant of the clinical effectiveness of RPM interventions. For example, the patient experience afforded by a given intervention is a determinant of both adoption of and adherence to that intervention, ultimately affecting its clinical impact. In a recent review, for instance, low patient adherence was identified as the main factor associated with ineffective RPM services [ 23 ].
  • Number 7: Similarly, staff experience can be a determinant of clinical effectiveness. Experience-related issues, such as alarm fatigue , contribute to medical errors and lower the quality of care delivery [ 24 ].
  • Number 8: Staff experience can also impact the cost of care. For example, the time effort required for the use of a given intervention can constitute a source of extra costs. More indirectly, low staff satisfaction and excessive workload can increase health care staff turnover, resulting in additional expenses at the level of the health system.

Overall, the overview in Figure 1 can help us grasp the nuances of the role of patient and staff experience on the overall impact of RPM interventions, as well as the importance of measuring experience factors, not only in isolation, but also in relation to other dimensions of care quality. In this review, we therefore covered a broad range of experience-relevant factors, including both experiential determinants (eg, usability) and manifestations (eg, adherence). Overall, this study aimed to obtain a comprehensive set of experience constructs and corresponding measurement instruments used in contemporary RPM research and to propose an initial set of guidelines for improving methodological standardization in this domain.

Protocol Registration and PRISMA Guidelines

The study protocol was registered in the PROSPERO (International Prospective Register of Systematic Reviews) database (CRD42021250707). This systematic review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PRISMA checklist is provided in Multimedia Appendix 1 [ 25 ].

Criteria for Study Eligibility

Our study population consisted of adult (≥18 years old) patients and staff members involved as participants in reported RPM evaluations. Full-text papers reporting instances of patient and staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility.

For the scope of our review, we considered as RPM any intervention possessing the following characteristics:

  • Sensor-based patient monitoring, intended as the use of at least 1 sensor to collect patient information at a distance. Therefore, we excluded interventions that were purely based on the collection of “sensor-less” self-reported measures from patients. This is because we believe the use of sensors constitutes a key element of RPM and one that strongly contributes to experiential aspects in this domain. However, we adopted a broad definition of “sensor,” considering as such, for instance, smartphone cameras (eg, postoperative wound-monitoring apps) and analog scales or thermometers (eg, interventions relying on patients submitting manually entered weights or temperatures). By “at a distance,” we meant not only cases in which data were transferred from nonclinical environments, such as home monitoring, but also cases such as tele–intensive care units (tele-ICUs), in which data were transferred from one clinical environment to another. Furthermore, we included interventions relying on both continuous and intermittent monitoring.
  • Clinical decision-making as an intended use of remotely collected data. Therefore, we excluded interventions in which the collected data were meant to be used exclusively for research purposes and not as a stage of development of an RPM intervention to be adopted in patient care. For instance, we excluded cases in which the remotely collected patient data were only used to test research hypotheses unrelated to the objective of implementing RPM interventions (eg, for drug development purposes). This is because in this review we were interested in RPM as a tool for the provision of remote patient care, rather than as an instrument for research. We also excluded interventions in which patients themselves were the only recipients of the collected data and no health care professional was involved in the data analysis and use.

Furthermore, we excluded:

  • Evaluations of attitudes, not interventions: contributions in which only general attitudes toward RPM in abstract were investigated, rather than 1 or more specific RPM interventions.
  • Not reporting any evaluation: contributions not focusing on the evaluation of 1 or more specific RPM interventions, for instance, papers providing theoretical perspectives on the field (eg, research frameworks or theoretical models).
  • Evaluation of technology, not interventions: contributions only focused on evaluating RPM-related technology, for instance, papers focused on testing sensors, software, or other service components in isolation rather than as a part of any specific RPM intervention.
  • Not just RPM: contributions not specifically focused on RPM but including RPM interventions in their scope of research, for instance, papers reporting on surveys obtained from broad cohorts of patients (including RPM recipients) in a noncontrolled way. An example of such contributions would be represented by studies focusing on patient experience with mobile health apps in general, covering both interventions involving RPM and interventions not including any kind of patient monitoring, without a clear way to distinguish between the 2 kinds of interventions in the contribution results. This was chosen in order to maintain the review focus on RPM interventions. Instead, papers including both RPM and other forms of care provisions within the same intervention were included, as well as papers comparing RPM to non-RPM interventions in a controlled way.
  • Primary care intervention only: interventions only involving general practitioners (GPs) and other primary care practitioners as health care providers of the RPM intervention. This is because we expected marked differences between the implementation of RPM in primary care and at other levels of care, due to deep dissimilarities in settings, workflows, and routines. Examples of RPM interventions only involving primary care providers included kiosk systems (for which a common measuring point was provided to many patients) or pharmacy-managed medication-monitoring programs. RPM interventions involving primary care providers and providers from higher levels of care, however, were included in the review.
  • Staff-to-staff intervention: contributions reporting on interventions exclusively directed at staff, for instance, papers reporting on RPM methods aimed at monitoring stress levels of health care workers.
  • Target group other than patient or staff: contributions aimed at collecting experience measures in target groups other than patients or staff, for instance, papers investigating the experience in RPM for informal caregivers.

Search Method

To identify relevant publications, the following electronic databases were searched: (1) Medline (PubMed) and (2) EMBASE. Search terms included controlled terms from Medical Subject Headings (MeSH) in PubMed and Emtree in EMBASE, as well as free-text terms. Query term selection and structuring were performed collaboratively by authors VP, HCMO, and PG (who is a clinical librarian at the Leiden University medical library). The full search strategies are reported in Multimedia Appendix 2 . Because the aim of the review was to paint a contemporary picture of experience measures used in RPM, only studies published starting from January 1, 2011, were included.

Study Selection

Study selection was performed by VP and HCMO, who used Rayyan, an online research tool for managing review studies [ 26 ], to independently screen both titles and abstracts in the initial screening and full texts in the final screening. Discrepancies were solved by discussion. A flowchart of study selection is depicted in Figure 2 .

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Quality Appraisal

The objective of this review was to provide a comprehensive overview of the relevant literature, rather than a synthesis of specific intervention outcomes. Therefore, no papers were excluded based on the quality appraisal, in alignment with similar studies [ 27 ].

Data Extraction and Management

Data extraction was performed independently by VP and HCMO. The extraction was performed in a predefined Microsoft Excel sheet designed by VP and HCMO. The sheet was first piloted in 15 included studies and iterated upon to optimize the data extraction process. The full text of all included studies was retrieved and uploaded in the Rayyan environment. Next, the full text of each included study was examined and relevant data were manually inputted in the predefined Excel sheet. Discrepancies were resolved by discussion. The following data types were extracted: (1) general study information (authors, title, year of publication, type of study, country or countries); (2) target disease(s), intervention, or clinical specialty; (3) used patient or staff experience evaluation instrument and measured experience construct; (4) evidence base, if indicated; and (5) number of involved staff or patient participants. By “construct,” we referred to the “abstract idea, underlying theme, or subject matter that one wishes to measure using survey questions” [ 28 ]. To identify the measured experience construct, we used the definition provided in the source contribution, whenever available.

Data Analysis

First, we analyzed the collected data through building general overviews depicting the kind of target participants (patients or staff) of the collected experience measures and their use over time. To organize the diverse set of results collected through the systematic review, we then performed a correspondence analysis (CA) [ 29 ], a multivariate statistical technique used for exploring and displaying relationships between categorical data. CA transforms a 2-way table of frequencies between a row and a column variable into a visual representation of relatedness between the variables. This relatedness is expressed in terms of inertia, which represents “a measure of deviation from independence” [ 30 ] between the row and column variables. Any deviations from the frequencies expected if the row and column variables were completely independent from each other contribute to the total inertia of the model. CA breaks down the inertia of the model by identifying mutually independent (orthogonal) dimensions on which the model inertia can be represented. Each successive dimension explains less and less of the total inertia of the model. On each dimension, relatedness is expressed in terms of the relative closeness of rows to each other, as well as the relative closeness of columns to each other. CA has been previously used to find patterns in systematic review data in the health care domain [ 31 ].

In our case, a 2-way table of frequencies was built based on how often any given instrument (eg, System Usability Scale [SUS]) was used to measure any given construct (eg, usability) in the included literature. Therefore, in our case, the total inertia of the model represented the amassed evidence base for relatedness between the collected experience constructs and measures, based on how they were used in the included literature.

To build the table of frequencies, the data extracted from the systematic review underwent a round of cleaning, in which the formulation of similar constructs was made more homogeneous: for instance, “time to review,” “time to response,” and “time for task” were merged under 1 label, “time effort.” An overview of the merged construct formulations is provided in Multimedia Appendix 3 . The result of the CA was a model where 2 dimensions contributed to more than 80% of the model’s inertia (explaining 44.8% and 35.7%, respectively) and where none of the remaining 59 dimensions contributed more than 7.3% to the remaining inertia. This gap suggests the first 2 dimensions to express meaningful relationships that are not purely based on random variation. A 2D solution was thus chosen.

General Observations

A total of 158 studies reporting at least 1 instance of patient or staff experience measuring in RPM were included in the review. The included studies covered a broad range of RPM interventions, most prominently diabetes care (n=30, 19%), implanted devices (n=12, 7.6%), and chronic obstructive pulmonary disease (COPD; n=10, 6.3%). From these studies, we reported 546 experience-measuring instances in RPM, covering 160 unique experience-measuring instruments used to measure 120 unique experience constructs.

Our results included 4 kinds of versatile (intended as nonspecific) experience-measuring instruments: the custom survey, log file analysis, protocol database analysis, and task analysis. All of them can be used for measuring disparate kinds of constructs:

  • By “custom survey,” we refer to survey instruments created to evaluate patient or staff experience in connection to 1 specific RPM study and only for that study.
  • By “log file analysis,” we refer to the set of experience assessment methods based on the automatic collection of data through the RPM digital infrastructures themselves [ 32 ]; examples are clicks, uploads, views, or other forms of interactions between users and the RPM digital system. This set of methods is typically used to estimate experience-relevant constructs, such as adherence and compliance.
  • By “protocol database analysis,” we refer to the set of experience assessment methods based on the manual collection of data performed by RPM researchers within a specific research protocol; an example of a construct measured with these instruments is the willingness to enroll.
  • By “task analysis,” we refer to the set of experience assessment methods based on the real-life observation of users interacting with the RPM system [ 33 ].

In addition to these 4 instruments, our results included a large number of specific instruments, such as standard indexes, surveys, and questionnaires. Overall, the most frequently reported instrument was, by far, the custom survey (reported in 155/546, 28.39%, instances), while the most frequently reported experience construct was satisfaction (85/546, 15.57%), closely followed by quality of life (71/546, 13%).

Target Participants and Timeline

We found large differences in the number of RPM-relevant experience constructs and instruments used for patients and for staff (see Figure 3 ). We also found instruments used for both patients and staff. Either these were broadly used instruments (eg, the SUS) that were administered to both patients and staff within the same study, or they were measures of interactions between patients and staff (eg, log file analysis instruments recording the number of remote contacts between patients and nursing assistants).

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RPM research appears to focus much more on patient experience than on staff experience, which was investigated in only 20 (12.7%) of the 158 included papers. Although it is possible that our exclusion criteria contributed to the paucity of staff experience measures, only 2 (0.1%) of 2092 studies were excluded for reporting on interventions directed exclusively at staff. Of the 41 (2%) studies we excluded for reporting on primary care interventions, we found 6 (15%) studies reporting on staff experience, a rate comparable to the one in the included sample. Furthermore, although our choice to exclude papers reporting on the RPM experience of informal caregivers might have contributed to a reduction in the number of collected constructs and measures, only 2 (0.1%) of 2092 studies were excluded for this reason, and the constructs used in these contributions were not dissimilar from the ones found in the included literature.

Among the included contributions that did investigate staff experience, we noticed that the number of participant staff members involved in the reported studies was only reported in a minority of cases (9/20, 45%).

Furthermore, a time-based overview of the collected results ( Figure 4 ) provided us with an impression of the expansion of the field in the time frame of interest for both patient and staff experience measures.

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Correspondence Analysis

The plotted results of the CA of experience constructs are shown in Figure 5 . Here, we discuss the outlook and interpretation of each dimension.

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The first dimension explained more than 44% of the model’s inertia. The contributions of this dimension showed which constructs had the most impact in determining its orientation: satisfaction (36%) and to a lesser extent adherence (26%) and quality of life (17%). On the negative (left) side of this dimension, we found constructs such as satisfaction, perceptions, and acceptability, which are associated with subjective measures of patient and staff experience and relate to how people feel or think in relation to RPM interventions. On the positive (right) side of this dimension, we found constructs such as adherence, compliance, and quality of life, which are associated with objectivized measures of patient and staff experience. By “objectivized measures,” we referred to measures that are meant to capture phenomena in a factual manner, ideally independently from personal biases and subjective opinions. Adherence and compliance, particularly, are often measured through passive collection of system data (eg, log file analysis) that reflect objective measures of the way patients or staff interact with RPM propositions. Even in the case of (health-related) quality of life, which can include subjective connotations and components, measures usually aim at capturing an estimation of the factual impact of health status on a person’s overall life quality.

In this sense, we attributed a distinction between how people feel versus what happens experience constructs to this first dimension. We noted that a similar distinction (between subjective vs objective measures of engagement in remote measurement studies) was previously proposed as a meaningful differentiation to structure “a field impeded by incoherent measures” [ 27 ].

The second dimension explained 35% of the model’s inertia. The contributions of this dimension showed which constructs had the most impact in determining its orientation: quality of life (62%) and adherence (24%). On the negative (bottom) side of this dimension, we found constructs such as quality of life, depression, and anxiety, which are often used as experiential descriptors of health outcomes. On the positive (top) side of this dimension, we found adherence, compliance, and frequency, which are often used as descriptions of the interactions of patients or staff with a specific (RPM) system. Thus, we attributed a distinction between health-relevant versus system-relevant experience constructs to this second dimension.

Based on the results of CA, we proposed a categorization of patient and staff experience–related constructs into 4 partly overlapping clusters. Coherent with the offered explanation of the 2 dimensions and in consideration of the constructs found in each area, we labeled these as service system–related experience measures, care-related experience measures, usage- and adherence-related experience measures, and health outcome–related experience measures. In Figure 6 , we display the results of the CA labeled through this categorization. In this second visualization, we presented the results on a logarithmic scale to improve the visibility of constructs close to the center of the axes. Overall, this categorization of patient and staff experience constructs used in the RPM literature paints a landscape of the contemporary research in this field, which shows a mix of influences from clinical disciplines, health psychology, human factors engineering, service design, user research, systems engineering, and computer science.

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A visualization of the reported patient experience constructs and some of the related measuring instruments, organized by the categories identified in the CA, is available in Figure 7 . A complete version of this visual can be found in Multimedia Appendix 4 , and an interactive version can be found in [ 34 ]. In this figure, we can note the limited crossovers between constructs belonging to different categories, with the exception of versatile instruments, such as custom survey and log file analysis.

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In the light of the collected findings, here we provide a set of recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it ( Figure 8 ). Although these recommendations are functional to strengthen the quality of individual research protocols, they are also meant to stimulate increased standardization in the field as a whole.

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Regarding what to measure, we provide 4 main recommendations. The first is to conduct structured evaluations of staff experience next to patient experience. Failing to evaluate staff experience leads to risks, such as undetected staff nonadherence, misuse, and overworking. Although new competencies need to be developed in order for staff to unlock the untapped potential of RPM [ 35 ], seamless integration with existing clinical workflows should always be pursued and monitored.

The second recommendation is to consider experience constructs in all 4 clusters indicated in Figure 6 , as these represent complementary facets of an overall experiential ensemble. Failing to do so exposes RPM evaluators to the risk of obtaining partial information (eg, only shedding light on how people feel but not on what happens in terms of patient and staff experience in RPM).

The third recommendation is to explicitly define and report a clear rationale regarding which aspects of patient and staff experience to prioritize in evaluations, depending on the goals and specificities of the RPM intervention. This rationale should ideally be informed by preliminary qualitative research and by a collaborative mapping of the expected relationships between patient and staff experience and other components of the Quadruple Aim framework for the RPM intervention at hand. Failing to follow this recommendation exposes RPM evaluators to the risk of obtaining results that are logically detached from each other and as such cannot inform organic improvement efforts. Virtuous examples of reporting a clear rationale were provided by Alonso-Solís et al [ 36 ] and den Bakker et al [ 37 ], who offered detailed accounts of the considerations used to guide the selection of included experience measures. Several existing frameworks and methods can be used to map such considerations, including the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework [ 38 ] and the logical framework [ 39 ]. A relatively lightweight method to achieve such an overview can also be represented by the use of Figure 1 as a checklist to inventory possible Quadruple Aim relationships for a specific RPM intervention.

The fourth recommendation is to routinely reassess the chosen set of experience measures after each iteration of the RPM intervention design. Initial assumptions regarding relationships between experience factors and other dimensions of intervention quality should be verified once the relevant data are available, and new ones should be formulated, if necessary. If the RPM intervention transitions from research stages to implementation as the standard of care, it is recommended to keep on collecting at least some basic experience measures for system quality monitoring and continuous improvement. Failing to update the set of collected measures as the RPM intervention progresses through successive development stages exposes RPM evaluators to the risk of collecting outdated information, hindering iterative improvement processes.

Regarding how to measure RPM patient and staff experience, we provide 2 main recommendations. The first is to work with existing, validated and widely used instruments as much as possible, only creating new instruments after a convincing critique against current ones. Figure 7 can be used to find existing instruments measuring a broad range of experience-relevant constructs so as to reduce the need to create new ones.

For instance, researchers interested in evaluating certain experience constructs, ideally informed by preliminary qualitative research, might consult the full version of Figure 7 (available in Multimedia Appendix 4 or as an interactive map in Ref. [ 34 ]) to find their construct of interest on the left side of the graph, follow the connecting lines to the existing relevant measures on the right, and identify the most frequently used ones. They can also use the visual to consider other possibly relevant constructs.

Alternatively, researchers can use the open access database of this review [ 40 ] and especially the “extracted data” Excel file to search for the construct of interest and find details of papers in the RPM domain in which the construct was previously measured.

Failing to follow this recommendation exposes RPM researchers to the risk of obtaining results that cannot be compared to meaningful benchmarks, compared to other RPM interventions, or be included in meta-analyses.

The second recommendation is to consider adopting automatic, “passive” methods of experience data collection, such as the ones we referred to in this review as log file analysis, so as to obtain actionable estimates of user behavior with a reduced need for patients and staff to fill tedious surveys [ 41 ] or otherwise provide active input. Failing to consider automatically collected log file data on patient and staff experience constitutes a missed opportunity in terms of both the quality and cost of evaluation data. We recognize such nascent data innovations as promising [ 42 ] but also in need of methodological definition, particularly in terms of an ethical evaluation of data privacy and access [ 43 , 44 ] in order to avoid exploitative forms of prosumption [ 45 ].

Principal Findings

This study resulted in a structured overview of patient and staff experience measures used in contemporary RPM research. Through this effort, we found that the research landscape has seen a sizeable growth in the past 10 years, that it is affected by a relative lack of focus on staff experience, and that the overall corpus of collected measures can be organized in 4 main categories (service system–related, care-related, usage- and adherence-related, and health outcome–related experience measures). Little to no consensus or standardization was found in the adopted methods. Based on these findings, a set of 6 actionable recommendations for RPM experience evaluators was provided, with the aim of improving the quality and standardization of experience-related RPM research. The results of this review align with and expand on recent contributions in the field, with particular regard to the work of White et al [ 27 ].

Directions for Further Research

Fruitful future research opportunities have been recognized in various areas of RPM experience measuring. Among them, we stress the need for comparative studies investigating patient and staff experience factors across different RPM interventions; for studies clarifying the use, potential, and limitations of log file analysis in this domain; and (most importantly) for studies examining the complex relationships between experience factors, health outcomes, and cost-effectiveness in RPM.

Ultimately, we recognize the need for integrated data strategies for RPM, intended as processes and rules that define how to manage, analyze, and act upon RPM data, including continuously collected experience data, as well as clinical, technical, and administrative data. Data strategies can represent a way to operationalize a systems approach to health care innovation, described by Komashie et al [ 46 ] as “a way of addressing health delivery challenges that recognizes the multiplicity of elements interacting to impact an outcome of interest and implements processes or tools in a holistic way.” As complex, adaptive, and partly automated systems, RPM interventions require sophisticated data strategies in order to function and improve [ 47 ]; continuous loops of system feedback need to be established and analyzed in order to monitor the impact of RPM systems and optimize their performance over time, while respecting patients’ and staff’s privacy. This is especially true in the case of RPM systems including artificial intelligence (AI) components, which require continuous monitoring and updating of algorithms [ 48 - 50 ]. We characterize the development of integrated, interdisciplinary data strategies as a paramount challenge in contemporary RPM research, which will require closer collaboration between digital health designers and health care professionals [ 51 - 53 ]. We hope to have provided a small contribution to this overall goal through our effort to structure the current landscape of RPM patient and staff experience evaluation.

Strengths and Limitations

We acknowledge both strengths and limitations of the chosen methodologies. The main strength of this review is its extensive focus, covering a large number of experience measures and RPM interventions. However, a limitation introduced by such a broad scope is the lack of differentiation by targeted condition, clinical specialty, RPM intervention characteristics, geographical area, or other relevant distinctions. Furthermore, limitations were introduced by choices, such as focusing exclusively on contributions in English and on nonprimary care and nonpediatric RPM interventions.

Contemporary patient and staff experience measuring in RPM is affected by a lack of consensus and standardization, affecting the quality of both primary and secondary research in this domain. This issue determines a critical knowledge gap in our understanding of the effectiveness of RPM interventions, which are known to bring about radical changes to the care experience of both patients and staff. Bridging this knowledge gap appears to be critical in a global context of urgent need for increased resource effectiveness across health care systems, including through the increased adoption of safe and effective RPM. In this context, this review offers support for RPM experience evaluators by providing a structured overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.


We gratefully acknowledge Jeroen Raijmakers, Francesca Marino, Lorena Hurtado Alvarez, Alexis Derumigny, and Laurens Schuurkamp for the help and advice provided in the context of this research.

Neither ChatGPT nor other generative language models were used in this research or in the manuscript preparation or review.

Data Availability

The data sets generated and analyzed during this review are available as open access in Ref. [ 40 ].

Authors' Contributions

VP conceived the study, performed the systematic review and data analysis, and was mainly responsible for the writing of the manuscript. HCMO collaborated on study design, performed independent screening of contributions, and collaborated on data analysis. RvK provided input to the study design and execution. PG supported query term selection and structuring. MK provided input on manuscript framing and positioning. DS provided input on the design, execution, and reporting of the correspondence analysis. All authors revised and made substantial contributions to the manuscript.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Full search strategies.

Overview of the merged construct formulations .

Reported patient experience constructs and associated measuring instruments (complete visual).

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Edited by T de Azevedo Cardoso; submitted 25.04.23; peer-reviewed by M Tai-Seale, C Nöthiger, M Gasmi ; comments to author 29.07.23; revised version received 25.08.23; accepted 20.02.24; published 22.04.24.

©Valeria Pannunzio, Hosana Cristina Morales Ornelas, Pema Gurung, Robert van Kooten, Dirk Snelders, Hendrikus van Os, Michel Wouters, Rob Tollenaar, Douwe Atsma, Maaike Kleinsmann. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.


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    Database Management System. Database Management System is an organizational system that enables proper organization of data in a database (Rocha, Correia, Adeli, Reis, & Costanzo, 2017). DBMS in A company allows data storage and transformation to information to facilitate the decision-making process.

  19. Essay

    Database systems can be easily configured to handle increasing amounts of data and can be scaled to meet the growing needs of the business. Additionally, database systems can be easily integrated with other systems and applications, providing businesses with a flexible and scalable technology infrastructure that can support growth and change.

  20. Database Management System Essay Examples

    The directory of free sample Database Management System papers offered below was put together in order to help embattled students rise up to the challenge. On the one hand, Database Management System essays we present here distinctly demonstrate how a really remarkable academic paper should be developed.

  21. (PDF) Role of Database Management Systems (DBMS) in Supporting

    This database design research model used Hevner's information systems research framework, starting with reference research, database lecture analysis, rigor and research relevance.

  22. Database Search

    Use Database Search to identify and connect to the best databases for your topic. In addition to digital content, you will find specialized search engines used in specific scholarly domains. Database Search. Related Services & Tools Tool HOLLIS. HOLLIS is Harvard Library's catalog. Search HOLLIS for books, articles, media and more.

  23. Essay Database Essay Database

    Database Systems Essay. Introduction Effective implementation of strategic plan is a critical task for the management. Before the implementation of this plan, it is essential for the business personnel to collect pertinent information about the strategic plan. In this concern, various database systems have been introduced.

  24. The Coolest Database System Companies Of The 2024 Big Data 100

    The Coolest Database System Companies Of The 2024 Big Data 100. By Rick Whiting. April 22, 2024, 2:00 PM EDT. Part 2 of CRN's Big Data 100 takes a look at the vendors solution providers should ...

  25. Opinion

    Guest Essay. The Supreme Court Has Already Botched the Trump Immunity Case. April 24, 2024, 5:02 a.m. ET. ... Within our constitutional system, the U.S. Supreme Court can still act effectively and ...

  26. Chaos in Dubai as UAE records heaviest rainfall in 75 years

    Chaos ensued in the United Arab Emirates after the country witnessed the heaviest rainfall in 75 years, with some areas recording more than 250 mm of precipitation in fewer than 24 hours, the ...

  27. NASA's Voyager 1 Resumes Sending Engineering Updates to Earth

    For the first time since November, NASA's Voyager 1 spacecraft is returning usable data about the health and status of its onboard engineering systems.The next step is to enable the spacecraft to begin returning science data again. The probe and its twin, Voyager 2, are the only spacecraft to ever fly in interstellar space (the space between stars).

  28. Opinion

    Ms. Cheney, a Republican, is a former U.S. representative from Wyoming and was vice chairwoman of the Jan. 6 select committee in the House of Representatives. On Thursday, the U.S. Supreme Court ...

  29. Hackers Broke Into Change Healthcare's Systems Days Before Cyberattack

    Photo: Patrick Sison/Associated Press. The hackers who attacked UnitedHealth Group 's Change Healthcare unit were in the company's networks for more than a week before they launched a ...

  30. Journal of Medical Internet Research

    Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through ...