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Journal articles on the topic 'Database Management Systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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1

Aparajitha., R. S. V., M. K. Kavitha, T. R. P. Monisha, T. S. B. Pavithra, and Vinoth P. Raja. "Database Management Systems." International Journal of Computer Applications 1, no. 8 (February 25, 2010): 73–76. http://dx.doi.org/10.5120/179-310.

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2

Krevit, Leah. "Database Management Systems." Medical Reference Services Quarterly 6, no. 4 (March 4, 1988): 65–68. http://dx.doi.org/10.1300/j115v06n04_07.

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3

Nnwobuike, Mbato Robinson, and Asagba Prince Oghenekaro. "Conventional database management systems." ACADEMICIA: AN INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL 11, no. 1 (2021): 889–903. http://dx.doi.org/10.5958/2249-7137.2021.00149.x.

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4

Feikis, J. "Secure database management systems." IEEE Potentials 18, no. 1 (1999): 17–19. http://dx.doi.org/10.1109/45.747239.

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5

Carey, Michael, and Laura Haas. "Extensible database management systems." ACM SIGMOD Record 19, no. 4 (December 1990): 54–60. http://dx.doi.org/10.1145/122058.122064.

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6

Li, Ying, John Smith, Tong Zhang, and Shih-Fu Chang. "Multimedia database management systems." Journal of Visual Communication and Image Representation 15, no. 3 (September 2004): 261–64. http://dx.doi.org/10.1016/j.jvcir.2004.08.004.

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7

Ghafoor, Arif. "Multimedia database management systems." ACM Computing Surveys 27, no. 4 (December 1995): 593–98. http://dx.doi.org/10.1145/234782.234798.

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8

Tuck, W. R. "Videotex and database management systems." Aslib Proceedings 38, no. 3 (March 1986): 85–92. http://dx.doi.org/10.1108/eb051002.

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9

Doherty, C. Gregory. "Database systems management and Oracle8." ACM SIGMOD Record 27, no. 2 (June 1998): 510–11. http://dx.doi.org/10.1145/276305.276358.

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10

Blakeley, José A. "Open object database management systems." ACM SIGMOD Record 23, no. 2 (June 1994): 520. http://dx.doi.org/10.1145/191843.191989.

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11

Bisland, Ralph B. "Database Management Systems: Understanding and Applying Database Technology." European Journal of Information Systems 1, no. 5 (May 1992): 367–68. http://dx.doi.org/10.1057/ejis.1992.13.

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12

Mershad, Khaleel, and Ali Hamieh. "SDMS: smart database management system for accessing heterogeneous databases." International Journal of Intelligent Information and Database Systems 14, no. 2 (2021): 115. http://dx.doi.org/10.1504/ijiids.2021.114513.

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Mershad, Khaleel, and Ali Hamieh. "SDMS: smart database management system for accessing heterogeneous databases." International Journal of Intelligent Information and Database Systems 14, no. 2 (2021): 115. http://dx.doi.org/10.1504/ijiids.2021.10035961.

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14

Keefe, T. F., W. T. Tsai, and J. Srivastava. "Database concurrency control in multilevel secure database management systems." IEEE Transactions on Knowledge and Data Engineering 5, no. 6 (1993): 1039–55. http://dx.doi.org/10.1109/69.250090.

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15

Mandapuram, Mounika, and Md Faruk Hosen. "The Object-Oriented Database Management System versus the Relational Database Management System: A Comparison." Global Disclosure of Economics and Business 7, no. 2 (December 31, 2018): 89–96. http://dx.doi.org/10.18034/gdeb.v7i2.657.

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Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

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16

Snehal Eknath Phule. "Graph Theory Applications in Database Management." International Journal of Scientific Research in Modern Science and Technology 3, no. 3 (March 16, 2024): 13–17. http://dx.doi.org/10.59828/ijsrmst.v3i3.190.

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Graph theory, which is a branch of discrete mathematics, has emerged as a powerful tool in various domains, including database management. This abstract investigates the ways in which ideas and methods from graph theory which can be applied to database systems, offering a thorough synopsis of their benefits. Complex interactions within data can be well-modeled by using the basic concepts of graph theory, such as nodes, edges, and relationships. Because of its capacity to represent and query complex relationships, graph databases have become more and more popular in the field of database administration. Graph databases are well-suited for situations such as social networks, recommendation systems, and interconnected data domains because they are excellent at representing and traversing relationships, in contrast to standard relational databases, which are excellent at managing structured data. The abstract delves into the key graph-based data models, such as property graphs, RDF (Resource Description Framework), explaining how they facilitate the representation of diverse relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets. The document highlights specific use cases where graph theory contributes to database management, including fraud detection, social network analysis, and recommendation systems. Additionally, it discusses the challenges associated with integrating graph databases into existing infrastructures and proposes solutions to address scalability and performance concerns. The abstract also touches upon the advancements in graph database query languages (Cypher) and SPARQL, showcasing their expressive power in querying complex relationships. The inclusion of graph-based indexing and optimization techniques demonstrates how database systems can efficiently handle queries involving large-scale graph data. As graph databases continue to evolve, this abstract concludes by outlining potential future directions in the intersection of graph theory and database management. It emphasizes the importance of ongoing research in developing scalable and efficient solutions for managing interconnected data, ultimately paving the way for more sophisticated and context-aware database systems relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets.

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17

Chapman, Karen, and Paul Brothers. "Database Coverage for Research in Management Information Systems." College & Research Libraries 67, no. 1 (January 1, 2006): 50–62. http://dx.doi.org/10.5860/crl.67.1.50.

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This study examines the database coverage of management information systems (MIS) journals and journal articles referenced by MIS researchers. Lists of titles and references were checked for coverage in twelve databases representing a variety of vendors: five multidisciplinary databases, four business databases, and three computer science or applied science databases. The best coverage of MIS journals is found in ABI/INFORM Global and Business Source Premier. The best coverage of articles referenced by MIS scholars is offered by the same two databases, although Business Source Premier offers significantly more full text. Business Source Premier and Web of Science provide the best coverage for any pair of the databases.

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18

Zasuhina, Ol'ga, and Egor Ershov. "DATABASE MANAGEMENT SYSTEMS IN MODERN REALITIES." Modern Technologies and Scientific and Technological Progress 2022, no. 1 (May 16, 2022): 217–18. http://dx.doi.org/10.36629/2686-9896-2022-1-217-218.

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19

Rakhimov, Bakhtiyar S., Feroza B. Rakhimova, and Sabokhat K. Sobirova. "Modeling database management systems in medicine." Journal of Physics: Conference Series 1889, no. 2 (April 1, 2021): 022028. http://dx.doi.org/10.1088/1742-6596/1889/2/022028.

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20

Dampney, C. N. G., and B. David Johnson. "Database Management Systems for the Geosciences." Exploration Geophysics 19, no. 1-2 (March 1988): 38–41. http://dx.doi.org/10.1071/eg988038.

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21

N, Sai Tanishq. "Machine Learning for Database Management Systems." International Journal of Engineering and Computer Science 9, no. 08 (August 19, 2020): 25132–47. http://dx.doi.org/10.18535/ijecs/v9i08.4520.

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Machine Learning (ML) is transforming the world with research breakthroughs that are leading to the progress of every field. We are living in an era of data explosion. This further improves the output as data that can be fed to the models is more than it has ever been. Therefore, prediction algorithms are now capable of solving many of the complex problems that we face by leveraging the power of data. The models are capable of correlating a dataset and its features with an accuracy that humans fail to achieve. Bearing this in mind, this research takes an in-depth look into the of the problem- solving potential of ML in the area of Database Management Systems (DBMS). Although ML hallmarks significant scientific milestones, the field is still in its infancy. The limitations of ML models are also studied in this paper.

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22

Liu, Xin, and Kenneth Salem. "Hybrid storage management for database systems." Proceedings of the VLDB Endowment 6, no. 8 (June 2013): 541–52. http://dx.doi.org/10.14778/2536354.2536355.

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23

Mohan, C., H. Pirahesh, W. G. Tang, and Y. Wang. "Parallelism in relational database management systems." IBM Systems Journal 33, no. 2 (1994): 349–71. http://dx.doi.org/10.1147/sj.332.0349.

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24

Rodríguez-Terol, A., M. O. Caraballo, D. Palma, B. Santos-Ramos, T. Molina, T. Desongles, and A. Aguilar. "Quality of interaction database management systems." Farmacia Hospitalaria (English Edition) 33, no. 3 (January 2009): 134–46. http://dx.doi.org/10.1016/s2173-5085(09)70079-6.

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25

Early, William F. "Database management systems for process safety." Journal of Hazardous Materials 130, no. 1-2 (March 17, 2006): 53–57. http://dx.doi.org/10.1016/j.jhazmat.2005.07.039.

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26

Arulraj, Joy, and Andrew Pavlo. "Non-Volatile Memory Database Management Systems." Synthesis Lectures on Data Management 11, no. 1 (February 12, 2019): 1–191. http://dx.doi.org/10.2200/s00891ed1v01y201812dtm055.

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27

Segev, Arie, and J. Leon Zhao. "Rule Management in Expert Database Systems." Management Science 40, no. 6 (June 1994): 685–707. http://dx.doi.org/10.1287/mnsc.40.6.685.

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28

Merzbacher, Matthew. "Teaching database management systems with Java." ACM SIGCSE Bulletin 32, no. 1 (March 2000): 31–35. http://dx.doi.org/10.1145/331795.331806.

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29

Sacco, Giovanni Maria, and Mario Schkolnick. "Buffer management in relational database systems." ACM Transactions on Database Systems 11, no. 4 (December 1986): 473–98. http://dx.doi.org/10.1145/7239.7336.

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30

Никольский, Д. Р., В. Ф. Барабанов, Н. И. Гребенникова, С. А. Коваленко, and А. М. Нужный. "ANALYSIS OF GRAPH DATABASE MANAGEMENT SYSTEMS." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА 19, no. 6(-) (December 20, 2023): 13–20. http://dx.doi.org/10.36622/vstu.2023.19.6.002.

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в современном информационном обществе обработка данных стала важным инструментом для принятия обоснованных решений и достижения успеха во многих областях деятельности. Для хранения данных используют разные виды систем управления базами данных (СУБД), каждая из которых обладает своими уникальными функциями, преимуществами и вариантами использования. Графовые СУБД предлагают большое количество инструментов и методов анализа и обработки данных. Был произведен обзор графовой СУБД Neo4j, мультимодальных СУБД Virtuoso и ArangoDB, с поддержкой графовой модели данных и графовой СУБД Memgraph. Каждая из СУБД обладает своим рядом особенностей и преимуществ при использовании. Были освещены основные свойства каждой из этих СУБД и приведены основные сценарии их использования. Рассмотрены преимущества и недостатки существующих предложенных на рынке СУБД и предпосылки к разработке метаграфовой СУБД. Предлагается модель данных для разрабатываемой СУБД и архитектура ее программного обеспечения, включая некоторые особенности ее реализации на уровне хранилища объектов. Разработанная модель предполагает оптимальное моделирование сложных процессов и обработку больших объемов сложных сетевых данных in the modern information society, data processing has become an important tool for making informed decisions and achieving success in many areas of activity. Different types of DBMS are used for data storage, each of which has its own unique functions, advantages and use cases. Graph DBMS offer a large number of tools and methods for data analysis and processing. This paper reviews the Neo4j graph DBMS, Virtuoso and ArangoDB multimodal DBMS with the support of the graph data model and the Memgraph graph DBMS. Each of the DBMS has its own number of special features and advantages when used. This paper highlights the main properties of each of these DBMS and provides the main scenarios for their use. The advantages and imperfections of the existing DBMS offered on the market and the prerequisites for the development of a metagraphic DBMS are considered. A data model for the DBMS being developed and its software architecture are proposed, including some features of its implementation at the object storage level. The developed model provides optimal modeling of complex processes and processing of large volumes of complex network data

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31

Zhang, Mingyi, Patrick Martin, Wendy Powley, and Jianjun Chen. "Workload Management in Database Management Systems: A Taxonomy." IEEE Transactions on Knowledge and Data Engineering 30, no. 7 (July 1, 2018): 1386–402. http://dx.doi.org/10.1109/tkde.2017.2767044.

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32

Hull, MEC, FF Cai, and DA Bell. "Buffer management algorithms for relational database management systems." Information and Software Technology 30, no. 2 (March 1988): 66–80. http://dx.doi.org/10.1016/0950-5849(88)90067-5.

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33

Gligor, Virgil, and Radu Popescu-Zeletin. "Transaction management in distributed heterogeneous database management systems." Information Systems 11, no. 4 (January 1986): 287–97. http://dx.doi.org/10.1016/0306-4379(86)90009-8.

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34

Ou, Yi, Peiquan Jin, and Theo Härder. "Flash-Aware Buffer Management for Database Systems." International Journal of Knowledge-Based Organizations 3, no. 4 (October 2013): 22–39. http://dx.doi.org/10.4018/ijkbo.2013100102.

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Classical buffer replacement policies, e. g., LRU, are suboptimal for database systems having flash disks for persistence, because they are not aware of the distinguished characteristics of flash-based storage devices. The authors introduce the basic principles of buffer management for such devices and present two efficient buffer algorithms that apply these principles. These algorithms significantly improve the performance of flash-based databases, as confirmed by our trace-driven performance study.

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35

Lings, BJ. "Database management." Data Processing 28, no. 1 (January 1986): 51. http://dx.doi.org/10.1016/0011-684x(86)90208-x.

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36

Deng, Tianle. "The application of database systems in information management." Applied and Computational Engineering 40, no. 1 (February 21, 2024): 33–42. http://dx.doi.org/10.54254/2755-2721/40/20230625.

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Database technology has always been a focal point of interest for enterprises and organizations in the field of information management. With the continuous growth and diversification of information, effective information management has become crucial. This paper aims to explore the extensive applications of database systems in information management. Firstly, the paper reviews relational and non-relational databases. Subsequently, it delves into the current applications of database systems in various domains, including enterprise management, retail, education, and government and public services. In the realm of enterprise management, database systems provide a solid foundation for information management by ensuring the timeliness, accuracy, and reliability of data. In the retail industry, they support inventory management, sales analysis, and enhance the user experience. In education, database systems are used for student information management, teaching data analysis, and online learning. In the government and public services sector, they facilitate information sharing and data transparency, while playing a critical role in crisis management and emergency response. This paper highlights the significance and diverse applications of database systems in different domains, offering insights into the current research trends and future prospects in this field.

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37

Markevich, D. V., V. V. Kharlanova, and A. D. Khomonenko. "INTEGRATION OF BUSINESS INTELLIGENCE SYSTEMS WITH DATABASE MANAGEMENT SYSTEMS IN TRANSPORT." H&ES Research 15, no. 2 (2023): 41–48. http://dx.doi.org/10.36724/2409-5419-2023-15-2-41-48.

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At present, the solution of various transport problems using information technologies is of great practical interest. To work with databases containing huge amounts of information, their structuring, thorough analysis and making optimal decisions, researchers and analysts actively use various systems, such as database management systems (DBMS) or business intelligence systems. Despite this, the integration of such systems, which can improve the quality of decisions, is still not widely used. Purpose: is to develop the integration technology between the business intelligence system and the DBMS, which allows you to work effectively with a huge amount of data when solving transport tasks. Methods: The integration of the systems under consideration is implemented using the PostgreSQL 11 DBMS and the Loginom Community analytical platform. Results: a full cycle of system integration has been completed, including the creation and filling of a database in the PostgreSQL 11 system, as well as its connection to the Loginom Community analytical platform for data structuring, analysis and further decision-making. Practical relevance: the main distinguishing feature of the implemented technology is the full compatibility of two systems initially responsible for different tasks: a database with an extensive ecosystem of available tools, and a platform that provides deep analytics capabilities for making optimal management decisions. The reasons and advantages of the integration of the above systems, as well as their application in solving specific transport problems, are presented. Discussion: when solving the problem of ensuring integration, the practical application of a DBMS and a business intelligence system is considered. Using PostgreSQL 11, two databases are formed, which are integrated with the Loginom Community analytical platform. It is advisable to continue further research in the areas of practical use of integration for the analytics of large database arrays, as well as in the interests of improving the quality of managerial decision-making in transport problems.

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38

Gillenson, Mark, Raymond D. Frost, and Michael G. Kilpatrick. "Database Pointers in Navigational and Object–Oriented Database Management Systems." Journal of Database Management 6, no. 4 (October 1995): 14–23. http://dx.doi.org/10.4018/jdm.1995100102.

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39

Andor, C. F. "Performance Benchmarking for NoSQL Database Management Systems." Studia Universitatis Babeș-Bolyai Informatica 66, no. 1 (July 1, 2021): 23. http://dx.doi.org/10.24193/subbi.2021.1.02.

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NoSQL database management systems are very diverse and are known to evolve very fast. With so many NoSQL database options available nowadays, it is getting harder to make the right choice for certain use cases. Also, even for a given NoSQL database management system, performance may vary significantly between versions. Database performance benchmarking shows the actual performance for different scenarios on different hardware configurations in a straightforward and precise manner. This paper presents a NoSQL database performance study in which two of the most popular NoSQL database management systems (MongoDB and Apache Cassandra) are compared, and the analyzed metric is throughput. Results show that Apache Cassandra outperformes MongoDB in an update heavy scenario only when the number of operations is high. Also, for a read intensive scenario, Apache Cassandra outperformes MongoDB only when both number of operations and degree of parallelism are high.

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40

Morel, Phyllis A. "Database management systems for blood bank applications." Immunohematology 4, no. 4 (2020): 82–84. http://dx.doi.org/10.21307/immunohematology-2019-1111.

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41

SELLERS, JMS, KG FRANCE, M. ZYTYNSKI, and CR ROBINSON. "APPLICATION OF DATABASE MANAGEMENT SYSTEMS. INFORMAL DISCUSSION." Proceedings of the Institution of Civil Engineers 84, no. 1 (February 1988): 167–70. http://dx.doi.org/10.1680/iicep.1988.159.

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42

Son, Sang Hyuk. "Replicated data management in distributed database systems." ACM SIGMOD Record 17, no. 4 (November 1988): 62–69. http://dx.doi.org/10.1145/61733.61738.

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43

Thuraisingham, Bhavani, and Hai-Ping Ko. "Concurrency control in trusted database management systems." ACM SIGMOD Record 22, no. 4 (December 1993): 52–59. http://dx.doi.org/10.1145/166635.166654.

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44

Anderson, T. Lougenia, and Gad Ariav. "Panel: user interfaces and database management systems." ACM SIGMOD Record 15, no. 2 (June 15, 1986): 293–94. http://dx.doi.org/10.1145/16856.16883.

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45

Dey, Debabrata, and Abraham Seidmann. "Benchmarking Decision Models for Database Management Systems." Information Systems Research 5, no. 3 (September 1994): 275–93. http://dx.doi.org/10.1287/isre.5.3.275.

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46

Pollard, Richard. "Microcomputer database management systems for bibliographic data." Electronic Library 4, no. 4 (April 1986): 230–40. http://dx.doi.org/10.1108/eb044703.

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47

Nica, Anisoara, Fabian M. Suchanek, and Aparna S. Varde. "Emerging multidisciplinary research across database management systems." ACM SIGMOD Record 39, no. 3 (February 8, 2011): 33–36. http://dx.doi.org/10.1145/1942776.1942786.

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48

Anjard, Ronald P. "The Basics of Database Management Systems (DBMS)." Industrial Management & Data Systems 94, no. 5 (June 1994): 11–15. http://dx.doi.org/10.1108/02635579410063261.

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49

Wang, Baoyu, David Coffield, and David Hutchison. "Database/domain approach to distributed systems management." Computer Communications 12, no. 6 (December 1989): 324–30. http://dx.doi.org/10.1016/0140-3664(89)90002-9.

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50

Dwyer, Patricia A., George D. Jelatis, and Bhavani M. Thuraisingham. "Multi-level security in database management systems." Computers & Security 6, no. 3 (June 1987): 245–51. http://dx.doi.org/10.1016/0167-4048(87)90105-2.

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