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Journal articles on the topic 'Database'

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Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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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

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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7

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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8

., 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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9

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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10

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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11

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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12

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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13

Tcheng, James E., Patricia B. Blunden, Carole C. Ross, Jessie S. Harrington, Richard S. Stack, Robert M. Califf, W. Edward Hammond, and David B. Pryor. "The Duke databank interventional cardiology database." Journal of the American College of Cardiology 17, no. 2 (February 1991): A5. http://dx.doi.org/10.1016/0735-1097(91)90988-l.

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14

Küzmič, Filip, Urban Šilc, Zdeňka Lososová, Ladislav Mucina, Milan Chytrý, Ilona Knollová, Stephan M. Hennekens, et al. "European Weed Vegetation Database – a gap-focused vegetation-plot database." Phytocoenologia 50, no. 1 (April 28, 2020): 93–100. http://dx.doi.org/10.1127/phyto/2019/0337.

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15

Thiele, Annett, Nadzeya Liaščynskaya, Tatsiana Broska, Susanne Bärisch, Arkadi Skuratovič, Dimitri Dubovik, Jazep Stepanovič, Galina Ermolenko, Oleg Sozinov, and Anastasia Sakovič. "Belarus Peatland Database." Phytocoenologia 45, no. 4 (December 1, 2015): 399–400. http://dx.doi.org/10.1127/phyto/2015/0092.

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16

Voigt, W., V. Brendler, K. Marsh, R. Rarey, H. Wanner, M. Gaune-Escard, P. Cloke, Th Vercouter, E. Bastrakov, and S. Hagemann. "Quality assurance in thermodynamic databases for performance assessment studies in waste disposal." Pure and Applied Chemistry 79, no. 5 (January 1, 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 database, Dortmund Databank (DDB), Common Thermodynamic Database (CTD), FreeGS, and Thermodynamic Reference Database (THEREDA)] including the situation for molten salts. The future role of the IUPAC standard for thermophysical and thermochemical data storage is emphasized.

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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

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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24

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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25

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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26

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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27

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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28

Al-Khanjari, Zuhoor A. "Metadata Extraction in Database Testing." Information Management and Business Review 5, no. 3 (March 30, 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 Generator tool, test case implementation with the Output Generator tool, test case validation with the Output Validator tool and report generation with the Report Generator tool.

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29

Ramesh, M. Ashwini. "Computer Searchable Chemical Database." Indian Journal of Applied Research 4, no. 1 (October 1, 2011): 453–54. http://dx.doi.org/10.15373/2249555x/jan2014/139.

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30

Nisar, Jasra, and Owais Noor Trumboo. "Database for Mobile Application." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 853–54. http://dx.doi.org/10.31142/ijtsrd11215.

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31

Tiwari, Vijay Kumar. "Oracle Database Backup Testing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2043–44. http://dx.doi.org/10.31142/ijtsrd11572.

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32

Prokhorov, Vadim, Tatiana Rogova, and Maria Kozhevnikova. "Vegetation Database of Tatarstan." Phytocoenologia 47, no. 3 (September 27, 2017): 309–13. http://dx.doi.org/10.1127/phyto/2017/0172.

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33

Kalníková, Veronika, and Helmut Kudrnovsky. "Gravel Bar Vegetation Database." Phytocoenologia 47, no. 1 (January 1, 2017): 109–10. http://dx.doi.org/10.1127/phyto/2017/0177.

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34

De Sanctis, Michele, Giuliano Fanelli, Alfred Mullaj, and Fabio Attorre. "Vegetation Database of Albania." Phytocoenologia 47, no. 1 (January 1, 2017): 107–8. http://dx.doi.org/10.1127/phyto/2017/0178.

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35

Gholizadeh, Hamid, Alireza Naqinezhad, and Milan Chytrý. "Hyrcanian Forest Vegetation Database." Phytocoenologia 49, no. 2 (March 29, 2019): 209–10. http://dx.doi.org/10.1127/phyto/2018/0315.

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36

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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37

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 of the chemical and crystallographic data contained in the electron diffraction computer database. This printed index is designed to be used independently or in conjunction with the computer database. The INDEX contains one line of data for each of approximately 70,000 inorganic compounds. Each line contains a number of data items including: element types present; the first ten low-angle d-spacings; chemical formula; mineral name; lattice type; chemical class indicator; and unique CD and PDF numbers.

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38

Elsabagh, Mahmoud. "NO SQL Database: Graph database." Egyptian Journal of Artificial Intelligence 1, no. 1 (June 1, 2022): 1–7. http://dx.doi.org/10.21608/ejai.2022.132961.1002.

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39

Said, Huwida E., Mario A. Guimaraes, Zakaria Maamar, and Leon Jololian. "Database and database application security." ACM SIGCSE Bulletin 41, no. 3 (August 25, 2009): 90–93. http://dx.doi.org/10.1145/1595496.1562909.

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40

Wayman, Douglas. "Database, What Database?" Journal of Electronic Resources Librarianship 35, no. 3 (July 3, 2023): 234–36. http://dx.doi.org/10.1080/1941126x.2023.2225013.

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41

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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42

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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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.2021.10035961.

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44

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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45

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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46

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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47

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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48

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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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.2017.10006044.

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50

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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