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Artículos de revistas sobre el tema "Databases"

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Publicado: 4 de junio de 2021
Última modificación: 8 de junio de 2024

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1

Kreil, David P. y Thure Etzold. "DATABANKS – a catalogue database of molecular biology databases". Trends in Biochemical Sciences 24, n.º 4 (abril de 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, n.º 5 (2012): 532–36. http://dx.doi.org/10.7763/ijet.2012.v4.426.

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3

Tengeri, Dávid y Ferenc Havasi. "Database Slicing on Relational Databases". Acta Cybernetica 21, n.º 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, n.º 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, n.º 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, n.º 9 (1986): 787–99. http://dx.doi.org/10.1241/johokanri.29.787.

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7

Finkelstein, S., M. Schkolnick y P. Tiberio. "Physical database design for relational databases". ACM Transactions on Database Systems 13, n.º 1 (marzo de 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 y Richard G. H. Cotton. "A database of locus-specific databases". Nature Genetics 39, n.º 4 (abril de 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 (30 de abril de 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, n.º 11 (1 de noviembre de 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, n.º 01 (25 de enero de 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, n.º 1 (1 de febrero de 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, n.º 1 (31 de diciembre de 2019): 189–90. http://dx.doi.org/10.1111/gcb.14869.

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14

Sordo, R. y U. Munari. "The Asiago Database of Spectroscopic Databases (ADSD)". Astronomy & Astrophysics 452, n.º 2 (22 de mayo de 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, n.º D1 (1 de diciembre de 2011): D1250—D1254. http://dx.doi.org/10.1093/nar/gkr1099.

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16

Li, Guoliang, Haowen Dong y Chao Zhang. "Cloud databases". Proceedings of the VLDB Endowment 15, n.º 12 (agosto de 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 y Bichitra Kalita. "Designing Graph Database Models from Existing Relational Databases". International Journal of Computer Applications 74, n.º 1 (26 de julio de 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 y M. Rabiei. "Database Consistency Using Update-Ordering in Distributed Databases". Journal of Algorithms & Computational Technology 1, n.º 1 (enero de 2007): 17–44. http://dx.doi.org/10.1260/174830107780122676.

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19

Qing Li y D. McLeod. "Conceptual database evolution through learning in object databases". IEEE Transactions on Knowledge and Data Engineering 6, n.º 2 (abril de 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, n.º 1 (1 de enero de 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 (20 de mayo de 2010): baq010. http://dx.doi.org/10.1093/database/baq010.

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22

Qadah y Irani. "A Database Machine for Very Large Relational Databases". IEEE Transactions on Computers C-34, n.º 11 (noviembre de 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, n.º 1 (18 de octubre de 2006): n/a. http://dx.doi.org/10.1002/meet.14504201268.

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24

Rashid, Awais y Peter Sawyer. "A database evolution taxonomy for object-oriented databases". Journal of Software Maintenance and Evolution: Research and Practice 17, n.º 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, n.º 4 (abril de 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, n.º 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, n.º 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 (octubre de 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 y Jasra Nisar. "Traditional Databases vs NOSQL". International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30 de junio de 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 y J. Thomas. "Global database views in a federation of autonomous databases". Journal of the Brazilian Computer Society 6, n.º 2 (1999): 00. http://dx.doi.org/10.1590/s0104-65001999000300005.

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31

Khalil, Omar Kassem, Aissa Boudjella y Brahim Belhouari Samir. "Comparison between Normalized Databases Implemented with Different Database Systems". Advanced Materials Research 774-776 (septiembre de 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 y Ali Hamieh. "SDMS: smart database management system for accessing heterogeneous databases". International Journal of Intelligent Information and Database Systems 14, n.º 2 (2021): 115. http://dx.doi.org/10.1504/ijiids.2021.114513.

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33

Mershad, Khaleel y Ali Hamieh. "SDMS: smart database management system for accessing heterogeneous databases". International Journal of Intelligent Information and Database Systems 14, n.º 2 (2021): 115. http://dx.doi.org/10.1504/ijiids.2021.10035961.

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34

Landsman, D., R. Gentleman, J. Kelso y B. F. Francis Ouellette. "DATABASE: A new forum for biological databases and curation". Database 2009 (5 de enero de 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 y Mourad Quzzani. "World Wide Database—integrating the Web, CORBA and databases". ACM SIGMOD Record 28, n.º 2 (junio de 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, n.º 3 (2015): 248–52. http://dx.doi.org/10.3169/itej.69.248.

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37

Lam, H., S. Y. W. Su y N. R. Koganti. "A physical database design evaluation system for CODASYL databases". IEEE Transactions on Software Engineering 14, n.º 7 (julio de 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, n.º 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, n.º 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 y Steven G. E. Marsh. "The IPD and IMGT/HLA database: allele variant databases". Nucleic Acids Research 43, n.º D1 (20 de noviembre de 2014): D423—D431. http://dx.doi.org/10.1093/nar/gku1161.

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41

Hu, Q. Lina, Ryan J. Ellis y Clifford Y. Ko. "Databases for surgical health services research: National cancer database". Surgery 165, n.º 3 (marzo de 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, n.º 22 (25 de septiembre de 2015): 7–10. http://dx.doi.org/10.15623/ijret.2015.0422003.

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43

Papazoglou, M. P. y L. Marinos. "On integrating database modeling constructs for software engineering databases". Microprocessing and Microprogramming 27, n.º 1-5 (agosto de 1989): 113–20. http://dx.doi.org/10.1016/0165-6074(89)90031-8.

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44

Chen, Ping-Kuen y Gwo-Dong Chen. "Making Database Schema Hierarchical for Visual Access to Databases". Knowledge and Information Systems 1, n.º 2 (mayo de 1999): 193–227. http://dx.doi.org/10.1007/bf03325098.

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45

Zaniewicz, Norbert y Andrzej Salamończyk. "Comparison of MongoDB, Neo4j and ArangoDB databases using the developed data generator for NoSQL databases". Studia Informatica. System and information technology 26, n.º 1 (7 de noviembre de 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 y Eric Pardede. "Transformation of Schema from Relational Database (RDB) to NoSQL Databases". Data 4, n.º 4 (27 de noviembre de 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, n.º 12 (diciembre de 2004): 407–9. http://dx.doi.org/10.1383/anes.5.12.407.55122.

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48

DAVISON, DANIEL. "Databases". Nature 329, n.º 6136 (septiembre de 1987): 194. http://dx.doi.org/10.1038/329194c0.

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49

Leaman, Adrian. "Databases". Facilities 4, n.º 9 (septiembre de 1986): 10–13. http://dx.doi.org/10.1108/eb006375.

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50

Berrington, James. "Databases". Anaesthesia & Intensive Care Medicine 8, n.º 12 (diciembre de 2007): 513–15. http://dx.doi.org/10.1016/j.mpaic.2007.09.011.

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