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Journal articles on the topic 'Distributed Database Systems'

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Published: 4 June 2021
Last updated: 6 September 2023

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1

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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2

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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3

Özsu, M. Tamer, and Patrick Valduriez. "Distributed and parallel database systems." ACM Computing Surveys 28, no. 1 (March 1996): 125–28. http://dx.doi.org/10.1145/234313.234368.

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4

Bell, D. A. "Application of distributed database systems." International Journal of Bio-Medical Computing 16, no. 3-4 (May 1985): 173–82. http://dx.doi.org/10.1016/0020-7101(85)90052-2.

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5

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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6

Maabreh, Khaled Saleh. "Performance Evaluation of Distributed Database Systems." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 9 (September 30, 2014): 4859–67. http://dx.doi.org/10.24297/ijct.v13i9.2383.

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Distributed database management systems manage a huge amount of data as well as large and increasingly growing number of users through different types of queries. Therefore, efficient methods for accessing these data volumes will be required to provide a high and an acceptable level of system performance. Â Data in these systems are varying in terms of types from texts to images, audios and videos that must be available through an optimized level of replication. Distributed database systems have many parameters like data distribution degree, operation mode and the number of sites and replication. These parameters have played a major role in any performance evaluation study. This paper investigates the main parameters that may affect the system performance, which may help with configuring the distributed database system for enhancing the overall system performance.
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7

Mullin, J. K. "Optimal semijoins for distributed database systems." IEEE Transactions on Software Engineering 16, no. 5 (May 1990): 558–60. http://dx.doi.org/10.1109/32.52778.

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8

Apers, Peter M. G. "Data allocation in distributed database systems." ACM Transactions on Database Systems 13, no. 3 (September 1988): 263–304. http://dx.doi.org/10.1145/44498.45063.

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9

Berra, PB, CYR Chen, A. Ghafoor, CC Lin, TDC Little, and D. Shin. "Architecture for distributed multimedia database systems." Computer Communications 13, no. 4 (May 1990): 217–31. http://dx.doi.org/10.1016/0140-3664(90)90120-6.

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10

Pu, Calton, Wenwey Hseush, Gail E. Kaiser, Kun-Lung Wu, and Philip S. Yu. "Divergence control for distributed database systems." Distributed and Parallel Databases 3, no. 1 (January 1995): 85–109. http://dx.doi.org/10.1007/bf01263658.

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11

Климаш, М., О. Костів, О. Гордійчук-Бублівська, and І. Чайковський. "ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ВИКОРИСТАННЯ РОЗПОДІЛЕНИХ БАЗ ДАНИХ В СИСТЕМАХ IIOT." Information and communication technologies, electronic engineering 2, no. 1 (August 2022): 12–18. http://dx.doi.org/10.23939/ictee2022.01.012.

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The Industrial Internet of Things (IIoT) determines the transformation of centralized systems into decentralized, more flexible, and efficient ones. Cloud technologies allow much more optimal use of IIoT resources. In the paper, the main features of the Industrial Internet of Things systems were investigated and the problems of smart manufacturing were analyzed. The necessity of using distributed architecture and cloud resources for flexible industrial systems organization was determined. In addition, the advantages of distributed computing for big data processing were established. The preference for relational databases over non-relational ones for data processing and the reliability of user requests service were defined. As well, the peculiarity of the relational database's operation was considered. For improving computational efficiency the use of a distributed database architecture was investigated. The benefits of involving cloud and distributed technologies in IIoT systems were determined. In this way, the possibility of choosing the most optimal parameters depending on the requirements for the industrial system productivity was defined. The opportunities for improving the quality of services in the Industrial Internet of Things by choosing the optimally distributed database architecture were determined.
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12

Puustjärvi, Juha. "Distributed management of transactions in heterogeneous distributed database systems." BIT 31, no. 3 (September 1991): 406–20. http://dx.doi.org/10.1007/bf01933259.

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13

HOSAIN, MD SHAZZAD, and MUHAMMAD ABDUL HAKIM NEWTON. "MULTI-KEY INDEX FOR DISTRIBUTED DATABASE SYSTEM." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (April 2005): 433–38. http://dx.doi.org/10.1142/s0218194005002075.

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In this paper we present a multi-key index model that enables us to search a record with more than one attribute values in distributed database systems. Indices provide fast and efficient access of data and so become a major aspect in centralized database systems. Most of the centralized database systems use B + tree or other types of index structures such as bit vector, graph structure, grid file etc. But in distributed database systems no index model is found in the literature. Therefore efficient access is a major problem in distributed databases. Our proposed index model avoids the query-flooding problem of existing system and thus optimizes network bandwidth.
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14

Liu, Yulin, Zengwen Yu, Xiaoguang Yuan, Wenjun Ke, Zhi Fang, Tianfeng Du, and Cuihong Han. "Assessing Database Contribution via Distributed Tracing for Microservice Systems." Applied Sciences 12, no. 22 (November 12, 2022): 11488. http://dx.doi.org/10.3390/app122211488.

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Microservice architecture is the latest trend in software systems development and transformation. In microservice systems, databases are deployed in corresponding services. To better optimize runtime deployment and improve system stability, system administrators need to know the contributions of databases in the system. For the high dynamism and complexity of microservice systems, distributed tracing can be introduced to observe the behavior of business scenarios on databases. However, it is challenging to evaluate the database contribution by combining the importance weight of business scenarios with their behaviors on databases. To solve this problem, we propose a business-scenario-oriented database contribution assessment approach (DBCAMS) via distributed tracing, which consists of three steps: (1) determining the importance weight of business scenarios in microservice system by analytic hierarchy process (AHP); (2) reproducing business scenarios and aggregating the same operations on the same database via distributed tracing; (3) calculating database contribution by formalizing the task as a nonlinear programming problem based on the defined operators and solving it. To the best of our knowledge, our work is the first research to study this issue. The results of a series of experiments on two open-source benchmark microservice systems show the effectiveness and rationality of our proposed method.
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15

Azizah, Nur, Veranda Hartajaya, and Sandi Riady. "Comparison Of Replication Strategies On Distributed Database Systems." International Journal of Cyber and IT Service Management 2, no. 1 (January 28, 2022): 20–29. http://dx.doi.org/10.34306/ijcitsm.v2i1.70.

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Today's computer applications have ever-increasing database system capabilities and performance. The growing amount of data that has to be processed in a business company makes centralized data processing ineffective. This inefficiency shows itself as a long reaction time. This is in direct opposition to the purpose of utilizing databases in data processing, which is to reduce the amount of time it takes to process data. Another database design is required to tackle this problem. Distributed database technology refers to an architecture in which several servers are linked together, and each one may process and fulfill local queries. Each participating server is responsible for serving one or more requests. In a multi-master replication scenario, all sites are main sites, and all main sites communicate with one another. The distributed database system comprises numerous linked computers that work together as a single system.
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16

ABORISADE, D. O., A. S. SODIYA, A. A. ODUMOSU, O. Y. ALOWOSILE, and A. A. ADEDEJI. "A SURVIVABLE DISTRIBUTED DATABASE AGAINST BYZANTINE FAILURE." Journal of Natural Sciences Engineering and Technology 15, no. 2 (November 22, 2017): 61–72. http://dx.doi.org/10.51406/jnset.v15i2.1684.

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Distributed Database Systems have been very useful technologies in making a wide range of information available to users across the World. However, there are now growing security concerns, arising from the use of distributed systems, particularly the ones attached to critical systems. More than ever before, data in distributed databases are more susceptible to attacks, failures or accidents owing to advanced knowledge explosions in network and database technologies. The imperfection of the existing security mechanisms coupled with the heightened and growing concerns for intrusion, attack, compromise or even failure owing to Byzantine failure are also contributing factors. The importance of survivable distributed databases in the face of byzantine failure, to other emerging technologies is the motivation for this research. Furthermore, It has been observed that most of the existing works on distributed database only dwelled on maintaining data integrity and availability in the face of attack. There exist few on availability or survibability of distributed databases owing to internal factors such as internal sabotage or storage defects. In this paper, an architecture for entrenching survivability of Distributed Databases occasioned by Byzantine failures is proposed. The proposed architecture concept is based on re-creating data on failing database server based on a set threshold value.The proposed architecture is tested and found to be capable of improving probability of survivability in distributed database where it is implemented to 99.6% from 99.2%.
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17

Buzuloiu, D., L. Dumitrescu, and D. Popescu. "Distributed real-time database approach for distributed computer control systems." IFAC Proceedings Volumes 27, no. 15 (September 1994): 65–70. http://dx.doi.org/10.1016/s1474-6670(17)45752-1.

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18

Welekar, Dr Rashmi. "Replication Data Concepts For Distributed Database Systems." Bioscience Biotechnology Research Communications 13, no. 14 (December 25, 2020): 344–46. http://dx.doi.org/10.21786/bbrc/13.14/79.

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19

J. Al-Houmaily, Yousef. "On Deferred Constraints in Distributed Database Systems." International Journal of Database Management Systems 5, no. 6 (December 31, 2013): 1–16. http://dx.doi.org/10.5121/ijdms.2013.5601.

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20

Bhargava, Bharat. "Reliability in distributed database systems (panel discussion)." ACM SIGMOD Record 14, no. 4 (May 1985): 420–22. http://dx.doi.org/10.1145/971699.318992.

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21

Ciciani, B., D. M. Dias, and P. S. Yu. "Analysis of replication in distributed database systems." IEEE Transactions on Knowledge and Data Engineering 2, no. 2 (June 1990): 247–61. http://dx.doi.org/10.1109/69.54723.

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22

Son, Sang Hyuk. "Replicated data management in distributed database systems." ACM SIGMOD Record 17, no. 4 (November 1988): 62–69. http://dx.doi.org/10.1145/61733.61738.

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23

Gavish, Bezalel, and Arie Segev. "Set query optimization in distributed database systems." ACM Transactions on Database Systems 11, no. 3 (August 1986): 265–93. http://dx.doi.org/10.1145/6314.6488.

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24

Yu, P. S., S. Balsamo, and Y. H. Lee. "Dynamic transaction routing in distributed database systems." IEEE Transactions on Software Engineering 14, no. 9 (1988): 1307–18. http://dx.doi.org/10.1109/32.6174.

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25

Johannsen, Wolfgang. "Transaction models for federative distributed database systems." Future Generation Computer Systems 7, no. 2-3 (April 1992): 329–37. http://dx.doi.org/10.1016/0167-739x(92)90020-c.

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26

Mohania, Mukesh K., and N. L. Sarda. "Rule allocation in distributed deductive database systems." Data & Knowledge Engineering 14, no. 2 (December 1994): 117–41. http://dx.doi.org/10.1016/0169-023x(94)90041-8.

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27

Samarati, Pierangela, Paul Ammann, and Sushil Jajodia. "Maintaining replicated authorizations in distributed database systems." Data & Knowledge Engineering 18, no. 1 (February 1996): 55–84. http://dx.doi.org/10.1016/0169-023x(95)00000-i.

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28

Wang, Baoyu, David Coffield, and David Hutchison. "Database/domain approach to distributed systems management." Computer Communications 12, no. 6 (December 1989): 324–30. http://dx.doi.org/10.1016/0140-3664(89)90002-9.

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29

Gupta, Ramesh, Jayant Haritsa, and Krithi Ramamritham. "Revisiting commit processing in distributed database systems." ACM SIGMOD Record 26, no. 2 (June 1997): 486–97. http://dx.doi.org/10.1145/253262.253366.

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30

Ozsu, M. T., and P. Valduriez. "Distributed database systems: where are we now?" Computer 24, no. 8 (August 1991): 68–78. http://dx.doi.org/10.1109/2.84879.

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31

Thomas, Gomer, Glenn R. Thompson, Chin-Wan Chung, Edward Barkmeyer, Fred Carter, Marjorie Templeton, Stephen Fox, and Berl Hartman. "Heterogeneous distributed database systems for production use." ACM Computing Surveys 22, no. 3 (September 1990): 237–66. http://dx.doi.org/10.1145/96602.96607.

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32

Lilien, L., and T. M. Chung. "Pessimistic quasipartitioning protocols for distributed database systems." IEEE Journal on Selected Areas in Communications 7, no. 3 (April 1989): 341–53. http://dx.doi.org/10.1109/49.16867.

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33

Kim, Kiyoung, Jonghyun Lee, and Songchun Moon. "Scheduling cooperative transactions in distributed database systems." Microprocessing and Microprogramming 40, no. 10-12 (December 1994): 743–46. http://dx.doi.org/10.1016/0165-6074(94)90031-0.

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34

Ram, Sudha. "A model for designing distributed database systems." Information & Management 17, no. 3 (October 1989): 169–80. http://dx.doi.org/10.1016/0378-7206(89)90018-9.

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35

Ramesh, Akula, and Dia L. Ali. "Query transformation in heterogeneous distributed database systems." Computers & Industrial Engineering 31, no. 1-2 (October 1996): 323–26. http://dx.doi.org/10.1016/0360-8352(96)00141-6.

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36

Ramarao, K. V. S. "Commitment in a partitioned distributed database." ACM SIGMOD Record 17, no. 3 (June 1988): 371–78. http://dx.doi.org/10.1145/971701.50247.

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37

Sidell, Jeff. "The Mariposa distributed database management system." ACM SIGMOD Record 25, no. 4 (December 1996): 76–80. http://dx.doi.org/10.1145/245882.245906.

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38

Ras, Zbigniew W. "Query Processing in Distributed Information Systems." Fundamenta Informaticae 15, no. 3-4 (August 1, 1991): 381–97. http://dx.doi.org/10.3233/fi-1991-153-411.

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In our model of a distributed information system, two information structures are maintained – the application database and the (routing) database used to route queries in a computer network. Any site of a distributed information system which does not understand some attribute values used in a query has to search for a site which can explain them. The information stored in routing database has a strong impact on the speed of this search. In [5], we propose that each site learns from its neighbors the descriptions of all unknown attribute values used in queries. These descriptions are represented in the form of rules and stored in dictionaries added to all sites of a distributed information system. This extended distributed system with dictionaries is called intelligent.
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39

Janech, Jan, and Tomas Baca. "Distributed Database Systems in Vehicular Ad-Hoc Network." Communications - Scientific letters of the University of Zilina 12, no. 3A (October 31, 2010): 50–54. http://dx.doi.org/10.26552/com.c.2010.3a.50-54.

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40

Janech, Jan, and Tomas Baca. "Distributed Database Systems in the Dynamic Networks Environment." Communications - Scientific letters of the University of Zilina 13, no. 4 (December 31, 2011): 72–77. http://dx.doi.org/10.26552/com.c.2011.4.72-77.

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41

Sun, W., W. Meng, and C. Yu. "Query Optimisation in Distributed Object-Oriented Database Systems." Computer Journal 35, no. 2 (April 1, 1992): 98–107. http://dx.doi.org/10.1093/comjnl/35.2.98.

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42

Kuhlenkamp, Jörn, Markus Klems, and Oliver Röss. "Benchmarking scalability and elasticity of distributed database systems." Proceedings of the VLDB Endowment 7, no. 12 (August 2014): 1219–30. http://dx.doi.org/10.14778/2732977.2732995.

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43

Gavish and Pirkul. "Computer and Database Location in Distributed Computer Systems." IEEE Transactions on Computers C-35, no. 7 (July 1986): 583–90. http://dx.doi.org/10.1109/tc.1986.1676799.

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44

Kim, Junguk L., and Hector J. Hernandez. "A network surveillance protocol for distributed database systems." Data & Knowledge Engineering 6, no. 5 (September 1991): 409–20. http://dx.doi.org/10.1016/0169-023x(91)90010-u.

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45

Larson, James A. "Four reference architectures for distributed database management systems." Computer Standards & Interfaces 8, no. 3 (January 1989): 209–21. http://dx.doi.org/10.1016/0920-5489(89)90013-5.

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46

McHugh, John, and Bhavani M. Thuraisingham. "Multilevel security issues in distributed database management systems." Computers & Security 7, no. 4 (August 1988): 387–96. http://dx.doi.org/10.1016/0167-4048(88)90579-2.

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47

Goyal, M. L., and G. V. Singh. "Access control in distributed heterogeneous database management systems." Computers & Security 10, no. 7 (November 1991): 661–69. http://dx.doi.org/10.1016/0167-4048(91)90124-v.

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48

Guh, K. C., and C. T. Yu. "Evaluation of transitive closure in distributed database systems." IEEE Journal on Selected Areas in Communications 7, no. 3 (April 1989): 399–407. http://dx.doi.org/10.1109/49.16872.

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49

Yeung, Chin-Fu, and Sheung-Lun Hung. "Deadlock resolution for distributed real-time database systems." Microelectronics Reliability 36, no. 6 (June 1996): 807–20. http://dx.doi.org/10.1016/0026-2714(95)00197-2.

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50

Lim, Jong T., and Song C. Moon. "Global checkpointing scheme for heterogeneous distributed database systems." Microprocessing and Microprogramming 32, no. 1-5 (August 1991): 747–54. http://dx.doi.org/10.1016/0165-6074(91)90432-s.

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