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Journal articles on the topic 'The Database'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 April 2022

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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

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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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

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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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

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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18

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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19

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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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

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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23

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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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

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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26

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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27

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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28

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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29

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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30

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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31

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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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

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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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

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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39

Alekseev, Konstantin. "Relational database problems." Кибернетика и программирование, no. 2 (February 2020): 7–18. http://dx.doi.org/10.25136/2644-5522.2020.2.34076.

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The relevance of this article lies in the fact that today's databases are the basis of numerous information systems. The information accumulated in them is extremely valuable material, and today database processing methods are widely spread in terms of extracting additional methods, knowledge from them, which are interconnected with generalization and various additional methods of information processing.The object of research in this work is relational databases and DBMS, the subject of research is the features of their use in applied programming.In accordance with the set goal, it is necessary to solve the following tasks:1) to consider the concept and essence of a relational database;2) to analyze the problematic aspects of relational databases in modern conditions. Relational databases are among the most widespread due to their simplicity and clarity at the creation stage and at the user level. It should also be noted that the main advantage of RDB is its compatibility with the main query language SQL, which is intuitive for users.Nevertheless, with all the variety of approaches, there are still some canons, violation of which greatly affects both the design of the database and its operation. For example, the problem of database normalization is very relevant. Neglecting normalization makes the database structure confusing and the database itself unreliable.Promising directions include the development of queries to a relational database using heuristic methods, as well as the method of accumulating previously optimized queries with subsequent verification of the derivability of the current query from the accumulated ones.Finally, a very slow decline in relational databases is probably happening. While they are still the primary storage medium, especially in large enterprise projects, they are gradually being replaced by non-relational solutions that will become the majority over time.

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40

Laksmita, Nadea Cipta, Erwin Apriliyanto, I. Wayan Pandu, and Kusrini Rini. "Comparison of NoSQL Database Performance with SQL Server Database on Online Airplane Ticket Booking." Indonesian Journal of Applied Informatics 4, no. 2 (August 9, 2020): 64. http://dx.doi.org/10.20961/ijai.v4i2.38956.

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<em>Flight ticket booking services have become more advanced, where bookings can be made through the android / iOS application and through a web browser, ticket reservations, no longer have to come to travel agents or come to the airport to book plane tickets. In this study using an online ticket booking database where one database uses the NoSQL database and another database uses SQL Server. The purpose of this research is to test the performance of NoSQL speed with SQL Server with the Insert, Delete and Select commands. The testing method uses 100 records, 500 records, 1000 records, and 5000 records, with each record being tested four times and then taken on average. The results of this study are that the NoSQL database Insert command has a speed 4 times faster than the SQL Server database for under 500 records, whereas above 500 NoSQL database records 5 times slower, the Delete NoSQL database command has a speed 3 times faster than the SQL database Server, and the command Select 1 NoSQL database table 55 times faster than SQL Server databases, while 2 NoSQL database tables are 18 times slower than SQL Server databases, while 3 NoSQL database tables are 10 times slower than SQL Server databases, whereas 4 database tables NoSQL is 16 times slower than SQL Server databases.</em>

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41

Heryanto, Ahmad, and Albert Albert. "Implementasi Sistem Database Terdistribusi Dengan Metode Multi-Master Database Replication." JURNAL MEDIA INFORMATIKA BUDIDARMA 3, no. 1 (March 1, 2019): 30. http://dx.doi.org/10.30865/mib.v3i1.1098.

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Databases are the main need for every computer application to store, process and modify data. One important problem faced in databases is the availability of adequate information technology infrastructure in managing and securing data contained in the database. Data stored on the database must have protection against threats and disturbances. Threats and disruptions can result from a variety of things, such as maintenance, data damage, and natural disasters. To anticipate data loss and damage, replication of the database system needs to be done. The replication mechanism used by researchers is multi-master replication. The replication technique is able to form a database cluster with replication time of fewer than 0.2 seconds.

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42

Wu, Yun Na, Jiang Shuai Li, and Jia Li Wang. "Design for Database of Energy Project Management Based on Portfolio." Advanced Materials Research 211-212 (February 2011): 62–67. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.62.

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With the continuous development of energy projects and the actual needs of the project, project portfolio management technique is known by people more and more. However, current databases of energy project management system are too different. This paper studies actual demand of energy project database, taking portfolio management theory as the basic, and use database modeling technology to build database’s conceptual data model, logical data model and physics data model based on the portfolio of energy project management. These models can be very good instruction of energy database design and construction, and will support energy project portfolio management system design to some guidance.

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43

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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44

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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45

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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46

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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47

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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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.2017.10006044.

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49

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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50

., 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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