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Journal articles on the topic 'Database Systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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1

Silberschatz, Avi, Michael Stonebraker, and Jeffrey D. Ullman. "Database systems." ACM SIGMOD Record 19, no. 4 (December 1990): 6–22. http://dx.doi.org/10.1145/122058.122059.

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2

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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3

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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4

Aparajitha., R. S. V., M. K. Kavitha, T. R. P. Monisha, T. S. B. Pavithra, and Vinoth P. Raja. "Database Management Systems." International Journal of Computer Applications 1, no. 8 (February 25, 2010): 73–76. http://dx.doi.org/10.5120/179-310.

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5

Maier, David. "Repackaging database systems." ACM Computing Surveys 28, no. 4es (December 1996): 83. http://dx.doi.org/10.1145/242224.242335.

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6

Kato, Toshikazu. "Kansei Database Systems." Journal of the Institute of Image Information and Television Engineers 52, no. 1 (1998): 49–53. http://dx.doi.org/10.3169/itej.52.49.

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7

Zdonik, Stanley B. "Incremental database systems." ACM SIGMOD Record 22, no. 2 (June 1993): 408–12. http://dx.doi.org/10.1145/170036.170101.

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8

Krevit, Leah. "Database Management Systems." Medical Reference Services Quarterly 6, no. 4 (March 4, 1988): 65–68. http://dx.doi.org/10.1300/j115v06n04_07.

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9

DeWitt, David, and Jim Gray. "Parallel database systems." Communications of the ACM 35, no. 6 (June 1992): 85–98. http://dx.doi.org/10.1145/129888.129894.

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10

DeWitt, David J., and Jim Gray. "Parallel database systems." ACM SIGMOD Record 19, no. 4 (December 1990): 104–12. http://dx.doi.org/10.1145/122058.122071.

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11

Graves, M., E. R. Bergeman, and C. B. Lawrence. "Graph database systems." IEEE Engineering in Medicine and Biology Magazine 14, no. 6 (November 1995): 737–45. http://dx.doi.org/10.1109/51.473268.

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12

Paton, Norman W., and Oscar Díaz. "Active database systems." ACM Computing Surveys 31, no. 1 (March 1999): 63–103. http://dx.doi.org/10.1145/311531.311623.

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13

Board, Raymond. "Distributed Database Systems." IASSIST Quarterly 16, no. 3 (January 31, 1993): 4. http://dx.doi.org/10.29173/iq59.

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14

Prakash, N. "Expert database systems." Information and Software Technology 32, no. 8 (October 1990): 572–73. http://dx.doi.org/10.1016/0950-5849(90)90153-i.

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15

Whittington, RP. "Expert database systems." Information and Software Technology 33, no. 4 (May 1991): 301. http://dx.doi.org/10.1016/0950-5849(91)90155-5.

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16

Garvey, M. "Distributed database systems." Information and Software Technology 35, no. 11-12 (November 1993): 704. http://dx.doi.org/10.1016/0950-5849(93)90095-k.

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17

van de Riet, R. P. "Expert database systems." Future Generation Computer Systems 2, no. 3 (September 1986): 191–99. http://dx.doi.org/10.1016/0167-739x(86)90015-4.

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18

Banerjee, Kyle. "Advanced database systems." Journal of the American Society for Information Science 49, no. 4 (1998): 388–89. http://dx.doi.org/10.1002/(sici)1097-4571(19980401)49:4<388::aid-asi16>3.0.co;2-b.

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19

Banerjee, Kyle. "Advanced database systems." Journal of the American Society for Information Science 49, no. 4 (April 1, 1998): 388–89. http://dx.doi.org/10.1002/(sici)1097-4571(19980401)49:4<388::aid-asi16>3.3.co;2-2.

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20

Motro, Amihai. "Cooperative database systems." International Journal of Intelligent Systems 11, no. 10 (December 7, 1998): 717–31. http://dx.doi.org/10.1002/(sici)1098-111x(199610)11:10<717::aid-int1>3.0.co;2-1.

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21

Ge, Zerui, Dumitrel Loghin, Beng Chin Ooi, Pingcheng Ruan, and Tianwen Wang. "Hybrid blockchain database systems." Proceedings of the VLDB Endowment 15, no. 5 (January 2022): 1092–104. http://dx.doi.org/10.14778/3510397.3510406.

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With the emergence of hybrid blockchain database systems, we aim to provide an in-depth analysis of the performance and trade-offs among a few representative systems. To achieve this goal, we implement Veritas and BlockchainDB from scratch. For Veritas, we provide two flavors to target the crash fault-tolerant (CFT) and Byzantine fault-tolerant (BFT) application scenarios. Specifically, we implement Veritas with Apache Kafka to target CFT application scenarios, and Veritas with Tendermint to target BFT application scenarios. We compare these three systems with the existing open-source implementation of BigchainDB. BigchainDB uses Tender-mint for consensus and provides two flavors: a default implementation with blockchain pipelining and an optimized version that includes blockchain pipelining and parallel transaction validation. Our experimental analysis confirms that CFT designs, which are typically used by distributed databases, exhibit much higher performance than BFT designs, which are specific to blockchains. On the other hand, our extensive analysis highlights the variety of design choices faced by the developers and sheds some light on the trade-offs that need to be done when designing a hybrid blockchain database system.
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22

Faerber, Frans, Alfons Kemper, Per-Åke Larson, Justin Levandoski, Tjomas Neumann, and Andrew Pavlo. "Main Memory Database Systems." Foundations and Trends® in Databases 8, no. 1-2 (2017): 1–130. http://dx.doi.org/10.1561/1900000058.

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23

Nnwobuike, Mbato Robinson, and Asagba Prince Oghenekaro. "Conventional database management systems." ACADEMICIA: AN INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL 11, no. 1 (2021): 889–903. http://dx.doi.org/10.5958/2249-7137.2021.00149.x.

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24

Paul, Johns, Shengliang Lu, and Bingsheng He. "Database Systems on GPUs." Foundations and Trends® in Databases 11, no. 1 (2021): 1–108. http://dx.doi.org/10.1561/1900000076.

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25

Özsu, M. Tamer. "Future of database systems." ACM Computing Surveys 28, no. 4es (December 1996): 85. http://dx.doi.org/10.1145/242224.242337.

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26

Batory, D. S., and M. Mannino. "Panel: Extensible database systems." ACM SIGMOD Record 15, no. 2 (June 15, 1986): 187–90. http://dx.doi.org/10.1145/16856.16873.

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27

Feikis, J. "Secure database management systems." IEEE Potentials 18, no. 1 (1999): 17–19. http://dx.doi.org/10.1109/45.747239.

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28

Ceri, Stefano, and Raghu Ramakrishnan. "Rules in database systems." ACM Computing Surveys 28, no. 1 (March 1996): 109–11. http://dx.doi.org/10.1145/234313.234362.

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29

Jenkins, T. "Review: Advanced Database Systems." Computer Bulletin 40, no. 4 (July 1, 1998): 30–31. http://dx.doi.org/10.1093/combul/40.4.30-b.

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30

Waltz, Timothy J., David (Chi‐Chung) Yen, and Sooun Lee. "Object‐oriented database systems." Industrial Management & Data Systems 95, no. 6 (August 1995): 8–17. http://dx.doi.org/10.1108/02635579510091278.

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31

Abadi, Daniel J., Peter A. Boncz, and Stavros Harizopoulos. "Column-oriented database systems." Proceedings of the VLDB Endowment 2, no. 2 (August 2009): 1664–65. http://dx.doi.org/10.14778/1687553.1687625.

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32

Gray, Jim. "Parallel database systems 101." ACM SIGMOD Record 24, no. 2 (May 22, 1995): 436. http://dx.doi.org/10.1145/568271.223864.

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33

Bancilhon, François, and Won Kim. "Object-oriented database systems." ACM SIGMOD Record 19, no. 4 (December 1990): 49–53. http://dx.doi.org/10.1145/122058.122063.

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34

Carey, Michael, and Laura Haas. "Extensible database management systems." ACM SIGMOD Record 19, no. 4 (December 1990): 54–60. http://dx.doi.org/10.1145/122058.122064.

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35

Li, Ying, John Smith, Tong Zhang, and Shih-Fu Chang. "Multimedia database management systems." Journal of Visual Communication and Image Representation 15, no. 3 (September 2004): 261–64. http://dx.doi.org/10.1016/j.jvcir.2004.08.004.

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36

Sangster, Alan. "Introduction to database systems." British Accounting Review 22, no. 1 (March 1990): 93–95. http://dx.doi.org/10.1016/0890-8389(90)90123-y.

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37

Djordjević-Kajan, Slobodanka. "Fundamentals of database systems." Microelectronics Journal 28, no. 5 (June 1997): 603–4. http://dx.doi.org/10.1016/s0026-2692(97)80960-3.

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38

Ghafoor, Arif. "Multimedia database management systems." ACM Computing Surveys 27, no. 4 (December 1995): 593–98. http://dx.doi.org/10.1145/234782.234798.

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39

MANAGAKI, Masao. "CAD/CAM Database Systems." Journal of the Society of Mechanical Engineers 91, no. 833 (1988): 331–37. http://dx.doi.org/10.1299/jsmemag.91.833_331.

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40

Gonzalo Leon, :. "Session G3: Database systems." Microprocessing and Microprogramming 32, no. 1-5 (August 1991): 745. http://dx.doi.org/10.1016/0165-6074(91)90431-r.

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41

McLeod, Dennis, and Paul Yanover. "On Intelligent Database Systems." Intelligent Systems in Accounting, Finance and Management 1, no. 4 (December 1992): 237–45. http://dx.doi.org/10.1002/j.1099-1174.1992.tb00024.x.

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42

Rigger, Manuel. "Automatically Testing Database Systems." Queue 21, no. 6 (December 31, 2023): 128–35. http://dx.doi.org/10.1145/3639449.

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The automated testing of DBMS is an exciting, interdisciplinary effort that has seen many innovations in recent years. The examples addressed here represent different perspectives on this topic, reflecting strands of research from software engineering, (database) systems, and security angles. They give only a glimpse into these research strands, as many additional interesting and effective works have been proposed. Various approaches generate pairs of related tests to find both logic bugs and performance issues in a DBMS. Similarly, other isolation-level testing approaches have been proposed. Finally, various fuzzing approaches use different strategies to generate mostly valid and interesting test inputs that extract various kinds of feedback from the DBMS.
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43

Sheth, Amit P., and James A. Larson. "Federated database systems for managing distributed, heterogeneous, and autonomous databases." ACM Computing Surveys 22, no. 3 (September 1990): 183–236. http://dx.doi.org/10.1145/96602.96604.

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44

Bisland, Ralph B. "Database Management Systems: Understanding and Applying Database Technology." European Journal of Information Systems 1, no. 5 (May 1992): 367–68. http://dx.doi.org/10.1057/ejis.1992.13.

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45

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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46

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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47

Magalhaes, Arlino, Jose Maria Monteiro, and Angelo Brayner. "Main Memory Database Recovery." ACM Computing Surveys 54, no. 2 (April 2021): 1–36. http://dx.doi.org/10.1145/3442197.

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Many of today’s applications need massive real-time data processing. In-memory database systems have become a good alternative for these requirements. These systems maintain the primary copy of the database in the main memory to achieve high throughput rates and low latency. However, a database in RAM is more vulnerable to failures than in traditional disk-oriented databases because of the memory volatility. DBMSs implement recovery activities (logging, checkpoint, and restart) for recovery proposes. Although the recovery component looks similar in disk- and memory-oriented systems, these systems differ dramatically in the way they implement their architectural components, such as data storage, indexing, concurrency control, query processing, durability, and recovery. This survey aims to provide a thorough review of in-memory database recovery techniques. To achieve this goal, we reviewed the main concepts of database recovery and architectural choices to implement an in-memory database system. Only then, we present the techniques to recover in-memory databases and discuss the recovery strategies of a representative sample of modern in-memory databases.
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48

Wałachowski, Kamil, and Grzegorz Kozieł. "Comparative analysis of database systems dedicated for Android." Journal of Computer Sciences Institute 15 (June 30, 2020): 126–32. http://dx.doi.org/10.35784/jcsi.2043.

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The article presents a comparative analysis of mobile databases dedicated for Android. The comparative analysis was carried out on the example of a relational SQLite database with selected nonrelational databases: Realm, ObjectBox and SnappyDB. Theoretical issues were discussed, such as stored data types. Research was carried out to check the performance of mobile databases in terms of: saving, editing, deleting, searching and sorting data. CPU and RAM usage were examined during saving data. The research also included a comparison the size of the database files on the internal disk.
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49

Sangwan, Pardeep, and Saurabh Bhardwaj. "A Structured Approach towards Robust Database Collection for Speaker Recognition." Global Journal of Enterprise Information System 9, no. 3 (September 27, 2017): 53. http://dx.doi.org/10.18311/gjeis/2017/16123.

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<p>Speaker recognition systems are classified according to their database, feature extraction techniques and classification methods. It is analyzed that there is a much need to work upon all the dimensions of forensic speaker recognition systems from the very beginning phase of database collection to recognition phase. The present work provides a structured approach towards developing a robust speech database collection for efficient speaker recognition system. The database required for both systems is entirely different. The databases for biometric systems are readily available while databases for forensic speaker recognition system are scarce. The paper also presents several databases available for speaker recognition systems.</p><p> </p>
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50

Keefe, T. F., W. T. Tsai, and J. Srivastava. "Database concurrency control in multilevel secure database management systems." IEEE Transactions on Knowledge and Data Engineering 5, no. 6 (1993): 1039–55. http://dx.doi.org/10.1109/69.250090.

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