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Journal articles on the topic 'Databases'

To see the other types of publications on this topic, follow the link: Databases.

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

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

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2

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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3

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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4

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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5

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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6

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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7

Finkelstein, S., M. Schkolnick, and P. Tiberio. "Physical database design for relational databases." ACM Transactions on Database Systems 13, no. 1 (March 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 (April 2007): 425. http://dx.doi.org/10.1038/ng0407-425.

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9

Nisa, Behjat U. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 845–48. http://dx.doi.org/10.31142/ijtsrd11214.

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10

BUREŠ, R. "Microbiological databases and databanks for biotechnology." Kvasny Prumysl 34, no. 11 (November 1, 1988): 331–33. http://dx.doi.org/10.18832/kp1988048.

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11

., Vinay Goyal. "REENGINEERING OF RELATIONAL DATABASES TO OBJECTORIENTED DATABASE." International Journal of Research in Engineering and Technology 03, no. 01 (January 25, 2014): 112–15. http://dx.doi.org/10.15623/ijret.2014.0301018.

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12

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

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13

Fraser, Lauchlan H. "TRY—A plant trait database of databases." Global Change Biology 26, no. 1 (December 31, 2019): 189–90. http://dx.doi.org/10.1111/gcb.14869.

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14

Sordo, R., and U. Munari. "The Asiago Database of Spectroscopic Databases (ADSD)." Astronomy & Astrophysics 452, no. 2 (May 22, 2006): 735–37. http://dx.doi.org/10.1051/0004-6361:20054619.

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15

Bolser, D. M., P. Y. Chibon, N. Palopoli, S. Gong, D. Jacob, V. D. D. Angel, D. Swan, et al. "MetaBase--the wiki-database of biological databases." Nucleic Acids Research 40, no. D1 (December 1, 2011): D1250—D1254. http://dx.doi.org/10.1093/nar/gkr1099.

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16

Li, Guoliang, Haowen Dong, and Chao Zhang. "Cloud databases." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3758–61. http://dx.doi.org/10.14778/3554821.3554893.

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As database vendors are increasingly moving towards the cloud data service, i.e., databases as a service (DBaaS), cloud databases have become prevalent. Compared with the early cloud-hosted databases, the new generation of cloud databases, also known as cloud-native databases, seek for higher elasticity and lower cost by developing new techniques, e.g., compute-storage disaggregation and the log is the database. To better harness the power of these cloud databases, it is important to study and compare the pros and cons of their key techniques. In this tutorial, we offer a comprehensive survey of cloud-native databases. Based on various system architectures, we introduce a taxonomy for the state-of-the-art cloud-native OLTP databases and OLAP databases, respectively. We then take a deep dive into their key techniques regarding storage management, transaction processing, analytical processing, data replication, serverless computing, database recovery, and security. Finally, we discuss the research challenges and opportunities.

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17

Bordoloi, Subhrajyoti, and Bichitra Kalita. "Designing Graph Database Models from Existing Relational Databases." International Journal of Computer Applications 74, no. 1 (July 26, 2013): 25–31. http://dx.doi.org/10.5120/12850-9303.

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18

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 (January 2007): 17–44. http://dx.doi.org/10.1260/174830107780122676.

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19

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

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20

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

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21

Zouberakis, M., C. Chandras, M. Swertz, D. Smedley, M. Gruenberger, J. Bard, K. Schughart, 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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22

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

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23

MacMullen, W. John. "Inter-database annotation linkages in model organism databases." Proceedings of the American Society for Information Science and Technology 42, no. 1 (October 18, 2006): n/a. http://dx.doi.org/10.1002/meet.14504201268.

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24

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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25

Stonebraker, Michael. "SQL databases v. NoSQL databases." Communications of the ACM 53, no. 4 (April 2010): 10–11. http://dx.doi.org/10.1145/1721654.1721659.

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26

NAGATSUKA, Takashi. "Databases. 5. Agricultural information databases." Journal of Information Processing and Management 29, no. 5 (1986): 421–33. http://dx.doi.org/10.1241/johokanri.29.421.

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27

MURATA, Yuko. "Databases. 10 Marketing information databases." Journal of Information Processing and Management 29, no. 10 (1987): 871–82. http://dx.doi.org/10.1241/johokanri.29.871.

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28

Kolomiyets, Svitlana V. "Meteor Databases in Astronomy." Proceedings of the International Astronomical Union 12, S325 (October 2016): 389–92. http://dx.doi.org/10.1017/s1743921317000527.

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AbstractThere are specific problems of databases in meteor science such as making meteor databases into the modern research tools. Special institutes and virtual observatories exist for the meteor data storage where the data is online and in open access. However, there are also numerous databases without the open access, such as for example, three radar databases: Kharkiv database with 250,000 meteor orbits in Ukraine, New Zealand database with 500,000 meteor orbits, and Canadian database with more than 3 million meteor orbits. One of the reasons the open access is absent for these databases could be the complexity in the copyright compliance. In the framework of the creation of the modern effective research tool in the meteor science, we discuss here the case of the Kharkiv meteor database.

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29

Trumboo, Owais Noor, and Jasra Nisar. "Traditional Databases vs NOSQL." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 68–70. http://dx.doi.org/10.31142/ijtsrd12961.

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30

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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31

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 database, approximately between 20 to 30% for 1NF flattening tables and 1NF decomposing table, and between (15% - 22%) and (9% - 22%) for 2NF and 3NF, respectively. The removal of the majority of anomalies is observed in the first normal form while fewer anomalies are removed in the next higher normal forms. For the same database implemented, the comparison between three different database systems shows approximately the same results with slight differences. These differences may be due to the nature, the size of the constraints and metadata on each database system.

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32

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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33

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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34

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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35

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 (June 1999): 594–96. http://dx.doi.org/10.1145/304181.304589.

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36

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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37

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 (July 1988): 1010–22. http://dx.doi.org/10.1109/32.42741.

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38

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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39

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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40

Robinson, James, Jason A. Halliwell, James D. Hayhurst, Paul Flicek, Peter Parham, and Steven G. E. Marsh. "The IPD and IMGT/HLA database: allele variant databases." Nucleic Acids Research 43, no. D1 (November 20, 2014): D423—D431. http://dx.doi.org/10.1093/nar/gku1161.

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41

Hu, Q. Lina, Ryan J. Ellis, and Clifford Y. Ko. "Databases for surgical health services research: National cancer database." Surgery 165, no. 3 (March 2019): 499–500. http://dx.doi.org/10.1016/j.surg.2018.01.006.

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42

., Alok Bajpai. "DATABASE CONNECTOR: A TOOL FOR MANIPULATION ON DIFFERENT DATABASES." International Journal of Research in Engineering and Technology 04, no. 22 (September 25, 2015): 7–10. http://dx.doi.org/10.15623/ijret.2015.0422003.

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43

Papazoglou, M. P., and L. Marinos. "On integrating database modeling constructs for software engineering databases." Microprocessing and Microprogramming 27, no. 1-5 (August 1989): 113–20. http://dx.doi.org/10.1016/0165-6074(89)90031-8.

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44

Chen, Ping-Kuen, and Gwo-Dong Chen. "Making Database Schema Hierarchical for Visual Access to Databases." Knowledge and Information Systems 1, no. 2 (May 1999): 193–227. http://dx.doi.org/10.1007/bf03325098.

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45

Zaniewicz, Norbert, and Andrzej Salamończyk. "Comparison of MongoDB, Neo4j and ArangoDB databases using the developed data generator for NoSQL databases." Studia Informatica. System and information technology 26, no. 1 (November 7, 2022): 61–72. http://dx.doi.org/10.34739/si.2022.26.04.

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NoSQL databases are gaining more and more popularity and are an important alternative to relational databases. Examples of such databases are Neo4j, MongoDB and ArangoDB. These databases are described in this article, compared with each other, and the performance results for adding an object to the database, deleting an object, searching and populating the database are presented. Results show that the fastest database is MongoDB, except for one measurement of removal.

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46

Alotaibi, Obaid, and Eric Pardede. "Transformation of Schema from Relational Database (RDB) to NoSQL Databases." Data 4, no. 4 (November 27, 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 aim of this paper is to propose transformation rules of RDB Schema to various NoSQL database schema, namely document-based, column-based and graph-based databases. The rules are applied based on the type of relationships that can appear in data within a database. As a proof of concept, we apply the rules into a case study using three NoSQL databases, namely MongoDB, Cassandra, and Neo4j. A set of queries is run in these databases to demonstrate the correctness of the transformation results. In addition, the completeness of our transformation rules are compared against existing work.

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47

Fairfield, John. "Databases." Anaesthesia & Intensive Care Medicine 5, no. 12 (December 2004): 407–9. http://dx.doi.org/10.1383/anes.5.12.407.55122.

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48

DAVISON, DANIEL. "Databases." Nature 329, no. 6136 (September 1987): 194. http://dx.doi.org/10.1038/329194c0.

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49

Leaman, Adrian. "Databases." Facilities 4, no. 9 (September 1986): 10–13. http://dx.doi.org/10.1108/eb006375.

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50

Berrington, James. "Databases." Anaesthesia & Intensive Care Medicine 8, no. 12 (December 2007): 513–15. http://dx.doi.org/10.1016/j.mpaic.2007.09.011.

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