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Journal articles on the topic 'Database'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Mallikharjuna Rao, N. "An Approach for Intelligent Database Maintenance of HLR and VLR Databases." International Journal of Engineering and Technology 4, no. 5 (2012): 532–36. http://dx.doi.org/10.7763/ijet.2012.v4.426.

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2

Tengeri, Dávid, and Ferenc Havasi. "Database Slicing on Relational Databases." Acta Cybernetica 21, no. 4 (2014): 629–53. http://dx.doi.org/10.14232/actacyb.21.4.2014.6.

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3

HAIBARA, Kazuhiro. "Database. 7. Credit information databases." Journal of Information Processing and Management 29, no. 7 (1986): 601–10. http://dx.doi.org/10.1241/johokanri.29.601.

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4

IBARAKI, Shingo. "Databases. 8. Trademark research database." Journal of Information Processing and Management 29, no. 8 (1986): 687–98. http://dx.doi.org/10.1241/johokanri.29.687.

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5

OHTAKI, Toshio. "Databases. 9. Meteorological information database." Journal of Information Processing and Management 29, no. 9 (1986): 787–99. http://dx.doi.org/10.1241/johokanri.29.787.

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6

Babu, P. Ajay, Radha Boddepalli, V. Vasantha Lakshmi, and G. Nageswara Rao. "DoD: Database of Databases – Updated Molecular Biology Databases." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 5, no. 5-6 (2005): 605–10. https://doi.org/10.3233/isb-00214.

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Database of Databases (DoD) is a collection of molecular biology databases extracted from Nucleic Acids Research, 2005 Database issue. DoD is constructed using javascript and html code. The 14 categories of 719 databases are provided with a search option, keyword help and database description linked to respective pages. Keyword help lists the search strings used to perform an individual search against categorized databases. Database description provides brief information about the database with a link to full-text article and main web page. DoD is available online and can be accessed at http:/

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7

Finkelstein, S., M. Schkolnick, and P. Tiberio. "Physical database design for relational databases." ACM Transactions on Database Systems 13, no. 1 (1988): 91–128. http://dx.doi.org/10.1145/42201.42205.

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8

Horaitis, Ourania, C. Conover Talbot, Manyphong Phommarinh, Kate M. Phillips, and Richard G. H. Cotton. "A database of locus-specific databases." Nature Genetics 39, no. 4 (2007): 425. http://dx.doi.org/10.1038/ng0407-425.

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9

., Vinay Goyal. "REENGINEERING OF RELATIONAL DATABASES TO OBJECTORIENTED DATABASE." International Journal of Research in Engineering and Technology 03, no. 01 (2014): 112–15. http://dx.doi.org/10.15623/ijret.2014.0301018.

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10

Samson, Clare. "Database evolution: Protein family and domain databases." Biochemist 31, no. 1 (2009): 52. http://dx.doi.org/10.1042/bio03101052.

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11

Fraser, Lauchlan H. "TRY—A plant trait database of databases." Global Change Biology 26, no. 1 (2019): 189–90. http://dx.doi.org/10.1111/gcb.14869.

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12

Sordo, R., and U. Munari. "The Asiago Database of Spectroscopic Databases (ADSD)." Astronomy & Astrophysics 452, no. 2 (2006): 735–37. http://dx.doi.org/10.1051/0004-6361:20054619.

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Bolser, D. M., P. Y. Chibon, N. Palopoli, et al. "MetaBase--the wiki-database of biological databases." Nucleic Acids Research 40, no. D1 (2011): D1250—D1254. http://dx.doi.org/10.1093/nar/gkr1099.

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14

Prof., Reshma Totare, Swamy Vishakha, Singh Anjali, and TejaswiniYadav. "MARKOV DECISION POLICY BASED DATABASE SECURITY." JournalNX - A Multidisciplinary Peer Reviewed Journal QIPCEI2K18 (April 29, 2018): 22–24. https://doi.org/10.5281/zenodo.1411153.

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An advanced database security system using Markov based policy is proposed. In large databases in this work, along with itâ€™s a difficult task to keep the data up to date where huge data of dynamic nature occurs. In e-commerce sites, large data lands up and the database might not get updated. This causes staleness in data and also, if the database undergoes updation, it might not be available for access during that time. Sometimes, updation is not possible without human intervention, hence Markov based policy is used which would perform fixed interval updates effectively and

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15

Tcheng, James E., Patricia B. Blunden, Carole C. Ross, et al. "The Duke databank interventional cardiology database." Journal of the American College of Cardiology 17, no. 2 (1991): A5. http://dx.doi.org/10.1016/0735-1097(91)90988-l.

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16

Shekhar Mishra. "Building Scalable Cloud Databases with Database Reliability Engineering." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1322–33. https://doi.org/10.32628/cseit251112125.

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This comprehensive article explores the evolution and implementation of Database Reliability Engineering (DBRE) in cloud environments, focusing on the transformation from traditional database management to modern cloud-based solutions. The article examines key aspects of scalable database architectures, including elastic scalability, serverless solutions, and advanced scaling techniques. The article investigates various strategies for ensuring database reliability, performance optimization, and cost management while addressing challenges in data distribution and consistency maintenance. Throug

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17

Voigt, W., V. Brendler, K. Marsh, et al. "Quality assurance in thermodynamic databases for performance assessment studies in waste disposal." Pure and Applied Chemistry 79, no. 5 (2007): 883–94. http://dx.doi.org/10.1351/pac200779050883.

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Performance assessment studies in underground disposal of radioactive or toxic waste need to consider all reactive interactions between waste and its surroundings. Thermodynamic equilibrium and reaction path calculations represent an important tool for this purpose. The reliability of the results depends first of all on the quality of the thermodynamic database used for the calculations. Several quality criteria of thermodynamic databases are discussed in connection with the characteristics of current database projects [Nuclear Energy Agency Thermochemical Database (NEA-TDB), Yucca Mountain da

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18

Küzmič, Filip, Urban Šilc, Zdeňka Lososová, et al. "European Weed Vegetation Database – a gap-focused vegetation-plot database." Phytocoenologia 50, no. 1 (2020): 93–100. http://dx.doi.org/10.1127/phyto/2019/0337.

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19

Khalil, Omar Kassem, Aissa Boudjella, and Brahim Belhouari Samir. "Comparison between Normalized Databases Implemented with Different Database Systems." Advanced Materials Research 774-776 (September 2013): 1827–32. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1827.

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This paper compares different levels of database normalization process in terms of anomalies removal and storage reduction. Several databases have been normalized up to Third Normal Form (1NF, 2NF and 3NF) in order to investigate the influence of the normalization on the database performance. They are implemented separately with different database systems such as MS Access, SQL Server and Oracle. The percentage of storage reduction and data anomalies are investigated for every normal form and database system. The results show that the data storage is significantly reduced over unnormalized dat

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20

Thiele, Annett, Nadzeya Liaščynskaya, Tatsiana Broska, et al. "Belarus Peatland Database." Phytocoenologia 45, no. 4 (2015): 399–400. http://dx.doi.org/10.1127/phyto/2015/0092.

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21

Bordoloi, Subhrajyoti, and Bichitra Kalita. "Designing Graph Database Models from Existing Relational Databases." International Journal of Computer Applications 74, no. 1 (2013): 25–31. http://dx.doi.org/10.5120/12850-9303.

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22

Shuhadah, W. N., M. Mat Deris, A. Noraziah, M. Y. Saman, and M. Rabiei. "Database Consistency Using Update-Ordering in Distributed Databases." Journal of Algorithms & Computational Technology 1, no. 1 (2007): 17–44. http://dx.doi.org/10.1260/174830107780122676.

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23

Qing Li and D. McLeod. "Conceptual database evolution through learning in object databases." IEEE Transactions on Knowledge and Data Engineering 6, no. 2 (1994): 205–24. http://dx.doi.org/10.1109/69.277766.

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24

Fowler, B. "BOOK REVIEWS: London Neurogenetics Database. Oxford Medical Databases." Journal of Neurology, Neurosurgery & Psychiatry 56, no. 1 (1993): 120. http://dx.doi.org/10.1136/jnnp.56.1.120-a.

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25

Zouberakis, M., C. Chandras, M. Swertz, et al. "Mouse Resource Browser--a database of mouse databases." Database 2010 (May 20, 2010): baq010. http://dx.doi.org/10.1093/database/baq010.

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26

Baqal, Hawzheen Mohammed Ali, and Mashkhal Abdalwahid Sidiq. "Graph Databases: Revolutionizing Database Design and Data Analysis." Current Journal of Applied Science and Technology 43, no. 11 (2024): 45–56. http://dx.doi.org/10.9734/cjast/2024/v43i114443.

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Traditional Relational databases have been in use for a while over the structured data and popular for a wide range of database management. However, with the increase in size and interconnection of data graph databases became more appealing due to its robustness and flexibility. Mathematical graph structures are used to represent, store and retrieve data in graph databases, which can be a paradigm shift for database design with powerful data capabilities. This article provides a basic overview of the state-of-the art graph databases, their use cases as well as some of the key advantages they c

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27

Qadah and Irani. "A Database Machine for Very Large Relational Databases." IEEE Transactions on Computers C-34, no. 11 (1985): 1015–25. http://dx.doi.org/10.1109/tc.1985.1676534.

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28

Kreil, David P., and Thure Etzold. "DATABANKS – a catalogue database of molecular biology databases." Trends in Biochemical Sciences 24, no. 4 (1999): 155–57. http://dx.doi.org/10.1016/s0968-0004(99)01363-8.

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29

MacMullen, W. John. "Inter-database annotation linkages in model organism databases." Proceedings of the American Society for Information Science and Technology 42, no. 1 (2006): n/a. http://dx.doi.org/10.1002/meet.14504201268.

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30

Rashid, Awais, and Peter Sawyer. "A database evolution taxonomy for object-oriented databases." Journal of Software Maintenance and Evolution: Research and Practice 17, no. 2 (2005): 93–141. http://dx.doi.org/10.1002/smr.310.

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31

Balakrishna, Boddu. "Serverless Databases Are the Future of Database Management." Journal of Scientific and Engineering Research 6, no. 1 (2019): 277–82. https://doi.org/10.5281/zenodo.14273369.

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The traditional way of managing databases involves setting up complex systems and constant maintenance, which is becoming challenging for modern apps that need to grow easily, be flexible, and save money. Serverless databases solve this problem by removing the need for manual management of the underlying infrastructure. This article discusses the main benefits of serverless databases, such as their ability to automatically adjust resources based on workload, reduce the amount of operational work, and speed up development processes. It also talks about the different types of serverless database

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32

van de Walle, Axel, Chiraag Nataraj, and Zi-Kui Liu. "The Thermodynamic Database Database." Calphad 61 (June 2018): 173–78. http://dx.doi.org/10.1016/j.calphad.2018.04.003.

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33

Priyanka, Gowda Ashwath Narayana Gowda. "SQL vs. NoSQL Databases: Choosing the Right Option for FinTech." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 100–104. https://doi.org/10.5281/zenodo.13950855.

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The paper discusses the critical decision-making in choosing between SQL and NoSQL databases for FinTech applications. FinTech, founded on large-scale data processing, transactional integrity, and real-time analytics, warrants robust and highly scalable database solutions. SQL databases are very suitable for applications such as payment processing, customer relationship management, and core banking systems because of their strong consistency, reliability, and mature ecosystem. On the other hand, NoSQL databases offer flexibility in handling unstructured data, horizontal scalability, and high a

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34

Vijay, Kumar Tiwari. "Oracle Database Backup Testing." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2043–44. https://doi.org/10.31142/ijtsrd11572.

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Testing the recovery of data on test systems is needed to come out with effective techniques for data recovery to be used in production environment. A good backup approach is to backup all data files available. A happy company and a happy employee are the ones who have a solid backup plan and recovery program. The above is to show ways of backing up essential test databases and methods to test these databases. Vijay Kumar Tiwari "Oracle Database Backup Testing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Iss

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35

Mighell, A. D., V. L. Himes, R. Anderson, and M. J. Carr. "D-spacing/formula index for compound identification using electron diffraction data." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 912–13. http://dx.doi.org/10.1017/s0424820100106624.

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NBS CRYSTAL DATA and the PDF-2 are large databases containing chemical, crystallographic and physical data on all classes of materials. From these databases, a derivative database has been prepared that is specifically designed for electron diffractionists. This database contains up to 60 calculated d-spacings for each of 70,000 inorganic compounds. An efficient computer program permits the identification of compounds by matching chemical and d-spacing data obtained from the unknown against the entire database.A Printed D-SPACING/FORMULA INDEX has been prepared that contains a critical subset

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36

Alotaibi, Obaid, and Eric Pardede. "Transformation of Schema from Relational Database (RDB) to NoSQL Databases." Data 4, no. 4 (2019): 148. http://dx.doi.org/10.3390/data4040148.

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Relational database has been the de-facto database choice in most IT applications. In the last decade there has been increasing demand for applications that have to deal with massive and un-normalized data. To satisfy the demand, there is a big shift to use more relaxed databases in the form of NoSQL databases. Alongside with this shift, there is a need to have a structured methodology to transform existing data in relational database (RDB) to NoSQL database. The transformation from RDB to NoSQL database has become more challenging because there is no current standard on NoSQL database. The ai

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37

Al-Khanjari, Zuhoor A. "Metadata Extraction in Database Testing." Information Management and Business Review 5, no. 3 (2013): 108–12. http://dx.doi.org/10.22610/imbr.v5i3.1033.

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The need for an automated testing tool to test the correctness of the database applications is crucial in our current day since databases play an important role in almost all organizations. Also, databaseâ€™s behavior need to be verified in order to avoid costly errors and false information being extracted from them. The main aim of this paper was to create a component-based tester called DBSoft that tests the correctness of database application systems. The DBSoft toolkit consists of five tools as follows: information collection with the Parser tool, test case generation with the Input Genera

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38

Maheswara, Reddy Basireddy. "Developing Tools to Compare Databases using Python." European Journal of Advances in Engineering and Technology 10, no. 2 (2023): 56–61. https://doi.org/10.5281/zenodo.13325121.

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Organizations frequently use several databases to store and manage their data in today's data-driven environment. It can be difficult to guarantee data integrity and consistency among various databases, though. This work investigates the creation of tools for database comparisons using Python, an effective and adaptable programming language. With the help of Python's vast library ecosystem, programmers may construct powerful tools that can connect to many kinds of databases, extract data, and carry out in-depth comparisons. In order to provide developers with a thorough manual for streamlining

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39

Blair, Jarrett, Rodger Gwiazdowski, Rodger Gwiazdowski, et al. "Towards a catalogue of biodiversity databases: An ontological case study." Biodiversity Data Journal 8 (March 27, 2020): e32765. https://doi.org/10.3897/BDJ.8.e32765.

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Biodiversity informatics depends on digital access to credible information about species. Many online resources host species' data, but the lack of categorisation for these resources inhibits the growth of this entire field. To explore possible solutions, we examined the (now retired) Biodiversity Information Projects of the World (BIPW) dataset created by the Biodiversity Information Standards (TDWG); this project, which ran from 2007-2015 (officially removed from the TDWG website in 2018) was an attempt at organising the Web's biodiversity databases into an indexed list. To do this, we appli

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40

Dobreva, Albena. "THE DATABASE DIRECTIVE IN THE LANDSCAPE OF MODERN DATABASES." Facta Universitatis, Series: Law and Politics, no. 1 (December 23, 2024): 235. https://doi.org/10.22190/fulp240810020d.

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The digital fraternity of data and technology has changed the existing and driven new legal regulations worldwide. Announced as an original European contribution, the sui generis right to database protection will not apply to certain data after the entry into force of Article 43 of the Data Act (2023). The specifics of the legal protection of electronic databases are determined by their technical nature. This paper offers results establishing the need to review the applicability of the Database Directive (1996) to the new generation of databases in the information society. Whether and how the

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41

Hnatchenko, Dmytro, Olena Kryvoruchko, and Oleksandr Vinichuk. "Mechanisms of replication and backup of databases in the cloud environment." Management of Development of Complex Systems, no. 58 (June 28, 2024): 146–52. http://dx.doi.org/10.32347/2412-9933.2024.58.146-152.

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The functionality of deploying databases on cloud computing environments, the main tools of the MS SQL SERVER DBMS for saving data in industrial databases, as well as the construction of a fault-tolerant database cluster on industrial servers are considered. All possible methods of data synchronization between the main and backup database, as well as methods of data backup to the cloud are considered. Researching the mechanisms of database synchronization and data backup, as well as the construction of a fault-tolerant cluster, will allow to consider in more detail the methods of data storage,

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42

Castilho, J. M. V. de, R. P. da Rocha, T. Härder, and J. Thomas. "Global database views in a federation of autonomous databases." Journal of the Brazilian Computer Society 6, no. 2 (1999): 00. http://dx.doi.org/10.1590/s0104-65001999000300005.

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43

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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44

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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45

Landsman, D., R. Gentleman, J. Kelso, and B. F. Francis Ouellette. "DATABASE: A new forum for biological databases and curation." Database 2009 (January 5, 2010): bap002. http://dx.doi.org/10.1093/database/bap002.

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46

Bouguettaya, Athman, Boualem Benatallah, Lily Hendra, James Beard, Kevin Smith, and Mourad Quzzani. "World Wide Database—integrating the Web, CORBA and databases." ACM SIGMOD Record 28, no. 2 (1999): 594–96. http://dx.doi.org/10.1145/304181.304589.

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47

Morifuji, Atsushi. "Making Most of Patent Information Databases (4); JPO's Database." Journal of the Institute of Image Information and Television Engineers 69, no. 3 (2015): 248–52. http://dx.doi.org/10.3169/itej.69.248.

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48

Lam, H., S. Y. W. Su, and N. R. Koganti. "A physical database design evaluation system for CODASYL databases." IEEE Transactions on Software Engineering 14, no. 7 (1988): 1010–22. http://dx.doi.org/10.1109/32.42741.

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49

Mandal, Pratap Chandra. "Customer databases and database marketing: roles in relationship marketing." International Journal of Business Forecasting and Marketing Intelligence 3, no. 3 (2017): 327. http://dx.doi.org/10.1504/ijbfmi.2017.085367.

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50

Mandal, Pratap Chandra. "Customer databases and database marketing: roles in relationship marketing." International Journal of Business Forecasting and Marketing Intelligence 3, no. 3 (2017): 327. http://dx.doi.org/10.1504/ijbfmi.2017.10006044.

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