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Journal articles on the topic 'Database management'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Patcharaprutipakorn, Tuangthong, Direk Thongaraim, and Nujcha Thatreenaranon. "Case Study: Database Management for Solid Waste Management Services." International Journal of Future Computer and Communication 6, no. 3 (September 2017): 133–37. http://dx.doi.org/10.18178/ijfcc.2017.6.3.505.

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2

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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3

Berg, John L. "Database management." Computer Standards & Interfaces 9, no. 1 (January 1989): 81. http://dx.doi.org/10.1016/0920-5489(89)90049-4.

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4

Petkov, Yulian Ivanov, and Alexandre Ivanov Chikalanov. "Innovative Proposals for Database Storage and Management." Mathematics and Informatics LXV, no. 1 (February 28, 2022): 45–52. http://dx.doi.org/10.53656/math2022-1-6-inn.

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At present, the problem of storing large data sets as a source of artificial intelligence acquires a geopolitical and strategic character. The most well-known and used type of databases so far are the relational (SQL databases) and nonrelational (NoSQL databases. The both approaches have some principle problems, which are described below. That publication presents two original approaches to overcoming some of these shortcomings. First one is Object-oriented model for storing data in a relational database. The second is Storage of non-relational data in a relational database according to previously freely created by the user models. Presented models were used as base for software development of more than ten middle and large size national and European scientific and industrial projects.

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5

Yasmeen, Mrs. "NOSQL Database Engines for Big Data Management." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 617–22. http://dx.doi.org/10.31142/ijtsrd18608.

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6

Dighe, NS, SA Nirmal, DS Musmade, and VV Dhasade. "Herbal database management." Systematic Reviews in Pharmacy 1, no. 2 (2010): 152. http://dx.doi.org/10.4103/0975-8453.75067.

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7

Short, Douglas D. "Microcomputer Database Management." Urologic Clinics of North America 13, no. 1 (February 1986): 39–45. http://dx.doi.org/10.1016/s0094-0143(21)01529-9.

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8

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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9

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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10

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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11

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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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.2021.10035961.

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13

Wahyudi, Jodi, Masduki Asbari, Ipang Sasono, Tias Pramono, and Dewiana Novitasari. "Database Management Education in MYSQL." Edumaspul: Jurnal Pendidikan 6, no. 2 (October 1, 2022): 2413–17. http://dx.doi.org/10.33487/edumaspul.v6i2.4570.

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The purpose of this research is to find out what is database management in MySQL or read My Sequel is a Database Management System often abbreviated as DBMS, MySQL is an open-source database management system. MySQL can be used to create and manage databases and their contents, from the smallest to the very large and to convey information to its users. MySQL is also included in the RDBMS or Relational Database Management system, where in its database structure so that when the data retrieval process uses the relational database method and becomes a liaison between the software and the database server. The thing to remember is that MySQL can be used for free.

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14

Lubis, Arif Ridho, Ferry Fachrizal, and Halim Maulana. "Database Management Optimization Using PostgreSQL Replication Database in Database System." Advanced Science Letters 23, no. 5 (May 1, 2017): 4132–35. http://dx.doi.org/10.1166/asl.2017.8286.

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15

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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16

Mandal, Pratap Chandra. "Roles of Customer Databases and Database Marketing in Customer Relationship Management." International Journal of E-Business Research 18, no. 1 (January 1, 2022): 1–12. http://dx.doi.org/10.4018/ijebr.315746.

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Development in information technology helps companies to build customer databases, perform database marketing, and do relationship management. The study discusses the various aspects of customer databases and database marketing and their roles in relationship management. It discusses the ways in which a customer database can be used to achieve best results. The study discusses the various issues of database marketing, ways of overcoming the issues, internal changes in an organization which should be made for implementing database marketing, and emphasizes that database marketing is not the solution to every issue. Organizations should analyze all these issues during CRM implementation. It also provides a direction for achieving the goals in relationship management. Organizations should be aware of and should consider all these aspects of customer databases and database marketing to develop customer relationships, to do CRM implementation, to achieve business excellence, and to succeed in the long run.

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17

MEWES, H. W., A. ELZANOWSKI, and D. G. GEORGE. "Protein sequence databases: database management, data structures and data access." Biochemical Society Transactions 17, no. 5 (October 1, 1989): 843–45. http://dx.doi.org/10.1042/bst0170843.

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18

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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19

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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20

George, David G., Lois T. Hunt, and Winona C. Barker. "Nucleic Acid Database Management." Science 235, no. 4796 (March 27, 1987): 1562. http://dx.doi.org/10.1126/science.235.4796.1562-a.

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21

Permaloff, Anne, and Carl Grafton. "Text-Oriented Database Management." PS: Political Science and Politics 23, no. 4 (December 1990): 586. http://dx.doi.org/10.2307/419900.

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22

Grafton, Carl, and Anne Permaloff. "Inexpensive Database Management Software." PS: Political Science and Politics 24, no. 2 (June 1991): 226. http://dx.doi.org/10.2307/419939.

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23

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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24

Stover, V. "Database management and design." IEEE Potentials 10, no. 2 (April 1991): 25–28. http://dx.doi.org/10.1109/45.84095.

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25

Adam, Nabil R. "Special Issue: Database Management." Journal of Management Information Systems 4, no. 2 (September 1987): 5–7. http://dx.doi.org/10.1080/07421222.1987.11517790.

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26

Garcia-Molina, H., and R. K. Abbott. "Reliable distributed database management." Proceedings of the IEEE 75, no. 5 (1987): 601–20. http://dx.doi.org/10.1109/proc.1987.13776.

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27

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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28

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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29

Cowen, Jay S., and Stephen C. Matchett. "THE CLINICAL MANAGEMENT DATABASE." Critical Care Clinics 15, no. 3 (July 1999): 481–97. http://dx.doi.org/10.1016/s0749-0704(05)70067-1.

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30

Othman, Raslan. "Interactive database management (IDM)." Computer Methods and Programs in Biomedicine 47, no. 3 (August 1995): 221–27. http://dx.doi.org/10.1016/0169-2607(95)01682-j.

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31

Zaror, Claudio A. "FoxPro database management system." Trends in Food Science & Technology 2 (January 1991): 103–4. http://dx.doi.org/10.1016/0924-2244(91)90639-z.

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32

Grafton, Carl, and Anne Permaloff. "Inexpensive Database Management Software." PS: Political Science & Politics 24, no. 02 (June 1991): 226–31. http://dx.doi.org/10.1017/s1049096500050794.

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33

Shivaramakrishnan, Kumar, and Oleh J. Tretiak. "Database management in autoradiography." Computerized Medical Imaging and Graphics 13, no. 1 (January 1989): 115–35. http://dx.doi.org/10.1016/0895-6111(89)90083-9.

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34

Edwards, c. Richard. "Pest Management Materials Database." American Entomologist 37, no. 2 (1991): 72–73. http://dx.doi.org/10.1093/ae/37.2.72.

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35

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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36

GEORGE, D. G., L. T. HUNT, and W. C. BARKER. "Nucleic Acid Database Management." Science 235, no. 4796 (March 27, 1987): 1562a. http://dx.doi.org/10.1126/science.235.4796.1562a.

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37

Permaloff, Anne, and Carl Grafton. "Text-Oriented Database Management." PS: Political Science & Politics 23, no. 04 (December 1990): 586–91. http://dx.doi.org/10.1017/s104909650003376x.

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38

Fong, Wilfred W. "Database management and control." Information Processing & Management 29, no. 6 (November 1993): 797–99. http://dx.doi.org/10.1016/0306-4573(93)90112-q.

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39

Robeson, Michael S., Devon R. O’Rourke, Benjamin D. Kaehler, Michal Ziemski, Matthew R. Dillon, Jeffrey T. Foster, and Nicholas A. Bokulich. "RESCRIPt: Reproducible sequence taxonomy reference database management." PLOS Computational Biology 17, no. 11 (November 8, 2021): e1009581. http://dx.doi.org/10.1371/journal.pcbi.1009581.

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Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt.

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40

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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41

Alsultanny, Yas. "Database management and partitioning to improve database processing performance." Journal of Database Marketing & Customer Strategy Management 17, no. 3-4 (September 2010): 271–76. http://dx.doi.org/10.1057/dbm.2010.14.

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42

Ankenbrand, Markus J., Lorenz Weber, Dirk Becker, Frank Förster, and Felix Bemm. "TBro: visualization and management ofde novotranscriptomes." Database 2016 (2016): baw146. http://dx.doi.org/10.1093/database/baw146.

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43

Bhatewara, Ankita, and Kalyani Waghmare. "Highly Scalable Network Management Solution Using Cassandra." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (October 30, 2014): 5085–89. http://dx.doi.org/10.24297/ijct.v13i10.2330.

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With the current emphasis on Big Data, NOSQL databases have surged in popularity. These databases are claimed to perform better than SQL databases. The traditional database is designed for the structured data and the complex query. In the environment of the cloud, the scale of data is very large, the data is non-structured, the request of the data is dynamic, these characteristics raise new challenges for the data storage and administration, in this context, the NOSQL database comes into picture. This paper discusses about some non-structured databases. It also shows how Cassandra is used to improve the scalability of the network compared to RDBMS.

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44

Margaret Dumebi Okpor, Kizito Eluemunor Anazia, and Daniel Ukpenusiowho. "A novel hybrid database security management technique." International Journal of Science and Research Archive 11, no. 2 (April 30, 2024): 1555–65. http://dx.doi.org/10.30574/ijsra.2024.11.2.0652.

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Since the advent of the internet and the digitization of information, more individuals and organizations have gained access to the internet, leading to a significant expansion in the size of our database systems. These databases are relied upon for day-to-day activities by individuals and organizations. Furthermore, the advancements in technology have increased the vulnerability of database management systems and enforcing adequate database security measures is of paramount importance to individuals and organizations. In this study, we deployed a hybrid approach to database security system that integrates two techniques: dynamic time-warping algorithm and voice recognition methods. The implementation follows Object-Oriented Analysis and Design procedures, utilizing SQL Management Studio 2014 for the backend and ASP.NET C# for the frontend. Our hybrid approach demonstrates enhanced database security compared to previously employed authentication measures.

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45

Navendu Mishra, Jayant Sawarkar, and Suraj Dubey. "Database Performance Management in Cloud." International Journal of Engineering and Management Research 10, no. 5 (October 20, 2020): 46–51. http://dx.doi.org/10.31033/ijemr.10.5.11.

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Managing large chain of Hotels and ERP database comprises of core areas such as HRMS & PIP.HRMS (Human Resource Management System), which further includes areas such as Soft Joining, Promotion, Transfer, Confirmation, Leave Attendance and Exit, etc. PIP (Payroll Information Portal), wherein employees can view their individual Salary details, submit investment declaration, Reimbursement claim & CTC structuring, etc. Management of Large Chain of Hotels and ERP Database in AWS Cloud involves continuous monitoring with regards to the areas such as Performance of resource usages and optimization techniques relating to the use of PL/SQL. High Availability (HA) of data is accomplished through the Backup and Recovery mechanism and security of the data by Encryption & Decryption mechanism.

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46

Kweon, Dae-Gon, and Sin-Hyeong Choi. "Sensor Data Management using Database." Journal of the Korea Academia-Industrial cooperation Society 10, no. 7 (July 31, 2009): 1608–13. http://dx.doi.org/10.5762/kais.2009.10.7.1608.

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47

G., Vignesh. "Optimization of Database Management System." International Journal of Business Intelligents 6, no. 2 (January 10, 2017): 42–44. http://dx.doi.org/10.20894/ijbi.105.006.002.004.

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48

Anonymous. "Bibliographic Database Management Program Released." Journal of Psychosocial Nursing and Mental Health Services 29, no. 12 (December 1991): 38. http://dx.doi.org/10.3928/0279-3695-19911201-19.

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49

Bobula, James A. "Database Management in Family Medicine." Primary Care: Clinics in Office Practice 12, no. 3 (September 1985): 483–96. http://dx.doi.org/10.1016/s0095-4543(21)00326-2.

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50

Ghosh, Sakti Pada. "Statisticians and Statistical Database Management." Calcutta Statistical Association Bulletin 40, no. 1-4 (January 1990): 125–43. http://dx.doi.org/10.1177/0008068319900511.

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