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Journal articles on the topic 'Distributed databases'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

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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2

Bonifati, Angela, Panos K. Chrysanthis, Aris M. Ouksel, and Kai-Uwe Sattler. "Distributed databases and peer-to-peer databases." ACM SIGMOD Record 37, no. 1 (March 2008): 5–11. http://dx.doi.org/10.1145/1374780.1374781.

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3

Frank, Andrew U. "Distributed Databases for Surveying." Journal of Surveying Engineering 111, no. 1 (March 1985): 79–88. http://dx.doi.org/10.1061/(asce)0733-9453(1985)111:1(79).

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4

Ammann, P., and S. Jajodia. "Rethinking integrity [distributed databases]." IEEE Concurrency 5, no. 4 (October 1997): 5–6. http://dx.doi.org/10.1109/4434.641618.

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5

Thuraisingham, Bhavani. "Security for Distributed Databases." Information Security Technical Report 6, no. 2 (June 2001): 95–102. http://dx.doi.org/10.1016/s1363-4127(01)00210-2.

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6

Ibrahim, Rafea Mohammed. "Data Synchronization for Distributed Heterogeneous Database." Journal of Electrical Systems 20, no. 4s (April 8, 2024): 2573–78. http://dx.doi.org/10.52783/jes.3165.

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Heterogeneous distributed database concurrent data is one of the important topics of heterogeneous databases that have important meaning. This paper discusses the mechanism of optimizing data synchronization for distributed heterogeneous database and trying to propose real solutions. Database synchronization is the exchange of records in databases on the condition that they are different. When modifications are made to a specific database, they will be applied to the database that has been synchronized in different environments and servers horizontally. The synchronization is either a one-way or two-way application and can be incidental, i.e. it is synchronous to the local network and asynchronous to the Internet due to its lagging technology. Heterogeneous distributed database systems consisting of a row storage database and a column storage database will face many difficulties, due to the inconsistent storage type such as: synchronization speed mismatch in data synchronization compared to a homogeneously distributed database system. Heterogeneous database management systems work on exchanging information for a package of various formats, accessing modification information in a heterogeneous database, maintaining the independence of the proposed system, and ensuring a good application of the proposed system in order to reach data synchronization between several packages of databases.

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7

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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8

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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9

Ghosh, Deb, and Mysore Ramaswamy. "Configuring Geographically Distributed Videotex Databases." Journal of Database Management 2, no. 4 (October 1991): 27–37. http://dx.doi.org/10.4018/jdm.1991100103.

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10

Deen, S. M., R. R. Amin, and M. C. Taylor. "Data Integration in Distributed Databases." IEEE Transactions on Software Engineering SE-13, no. 7 (July 1987): 860–64. http://dx.doi.org/10.1109/tse.1987.233497.

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11

Karr, Alan F., Xiaodong Lin, Ashish P. Sanil, and Jerome P. Reiter. "Secure Regression on Distributed Databases." Journal of Computational and Graphical Statistics 14, no. 2 (June 2005): 263–79. http://dx.doi.org/10.1198/106186005x47714.

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12

Knapp, Edgar. "Deadlock detection in distributed databases." ACM Computing Surveys 19, no. 4 (December 1987): 303–28. http://dx.doi.org/10.1145/45075.46163.

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13

Garcia-Molina, Hector, and Bruce Lindsay. "Research directions for distributed databases." ACM SIGMOD Record 19, no. 4 (December 1990): 98–103. http://dx.doi.org/10.1145/122058.122070.

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14

Menon, S. "Allocating fragments in distributed databases." IEEE Transactions on Parallel and Distributed Systems 16, no. 7 (July 2005): 577–85. http://dx.doi.org/10.1109/tpds.2005.77.

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15

Neiheiser, Ray, Roland Schmitz, Luciana Rech, and Manfredo Manfredini. "Efficient Fault-Tolerant Transactions for Distributed Graph Database." Singular Engenharia, Tecnologia e Gestão 1, no. 2 (October 19, 2019): 14–20. http://dx.doi.org/10.33911/singular-etg.v1i2.59.

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Through the ongoing trend in graph technologies due to the massive growth of linked data produced by social networks graph databases gained popularity. Replication, a common approach to increase availability in databases, is also used by diverse graph database solutions. Few approaches implementing fault-tolerance in graph databases have been proposed yet.This paper considers deferred update replication using atomic broadcast in order to implement fault-tolerance in distributed graph databases. The main contribution of this paper is a deferred update algorithm adapted to graph databases offering a more scalable and faster solution, showing a performance advantage of over 30\% compared to existing approaches.

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16

Abd Al-Majeed, Saran Akram. "Null Values Treatment in Distributed Databases." Iraqi Journal for Computers and Informatics 40, no. 1 (December 31, 2002): 55–64. http://dx.doi.org/10.25195/ijci.v40i1.226.

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There has been a great deal of discussion about null values in relational databases. The relational model was defined in 1969, and Nulls Was died in 1979. Unfortunately, there is not a generally agreeable solution for rull values problem. Null is a special marker which stands for a value undefined or unknown, which means thut ne entry has been made, a missing valuc mark is not a value and not of a date type and cannot be treated as a value by Database Management System (DBMS). As we know, distributed database users are more than a single database and data will be distributed among several data sources or sites, it must be precise data, the replication is allowed there, so complex problems will appear, then there will be need for perfect practical general approaches for treatment of Nulls. A distributed database system is designed, that is "Hotel reservation control system, based on different data sources at four site, each site is represented as a Hotel, for more heterogeneity different application programming languages there are five practical approaches, designed with their rules and algorithms for Null values treatment through the distributed database sites. (1), (2), (3). 14). 15), (9).

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17

Lee, Ju-Hong, Deok-Hwan Kim, Seok-Lyong Lee, Chin-Wan Chung, and Guang-Ho Cha. "Distributed similarity search algorithm in distributed heterogeneous multimedia databases." Information Processing Letters 75, no. 1-2 (July 2000): 35–42. http://dx.doi.org/10.1016/s0020-0190(00)00068-5.

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18

Kläbe, Steffen, Kai-Uwe Sattler, and Stephan Baumann. "PatchIndex: exploiting approximate constraints in distributed databases." Distributed and Parallel Databases 39, no. 3 (March 6, 2021): 833–53. http://dx.doi.org/10.1007/s10619-021-07326-1.

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AbstractCloud data warehouse systems lower the barrier to access data analytics. These applications often lack a database administrator and integrate data from various sources, potentially leading to data not satisfying strict constraints. Automatic schema optimization in self-managing databases is difficult in these environments without prior data cleaning steps. In this paper, we focus on constraint discovery as a subtask of schema optimization. Perfect constraints might not exist in these unclean datasets due to a small set of values violating the constraints. Therefore, we introduce the concept of a generic PatchIndex structure, which handles exceptions to given constraints and enables database systems to define these approximate constraints. We apply the concept to the environment of distributed databases, providing parallel index creation approaches and optimization techniques for parallel queries using PatchIndexes. Furthermore, we describe heuristics for automatic discovery of PatchIndex candidate columns and prove the performance benefit of using PatchIndexes in our evaluation.

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19

Rojek, Izabela, Dariusz Mikołajewski, Piotr Kotlarz, and Alžbeta Sapietová. "From Classical to Fuzzy Databases in a Production Enterprise." JUCS - Journal of Universal Computer Science 26, no. 11 (November 28, 2020): 1382–401. http://dx.doi.org/10.3897/jucs.2020.073.

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This article presents the evolution of databases from classical relational databases to distributed databases and data warehouses to fuzzy databases used in a production enterprise. This paper discusses characteristics of this kind of enterprise. The authors precisely define centralized and distributed databases, data warehouses and fuzzy databases. In the modern global world, many companies change their management strategy from the one based on a centralized database to an approach based on distributed database systems. Growing expectations regarding business intelligence encourage companies to deploy data warehouses. New solutions are sought as the demand for engineers' expertise continues to rise. The requested knowledge can be certain or uncertain. Certain knowledge does not any problems and is easy to obtain. However, uncertain knowledge requires new ways of obtaining, including the use of fuzzy logic. It is from where the fuzzy database approach takes its beginning. The above-mentioned strategies of a production enterprise were described herein as a case of special interest.

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20

Dykstra, Dave. "Comparison of the Frontier Distributed Database Caching System to NoSQL Databases." Journal of Physics: Conference Series 396, no. 5 (December 13, 2012): 052031. http://dx.doi.org/10.1088/1742-6596/396/5/052031.

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21

Kim, Won, Nat Ballou, Jorge F. Garza, and Darrell Woelk. "A distributed object-oriented database system supporting shared and private databases." ACM Transactions on Information Systems 9, no. 1 (January 3, 1991): 31–51. http://dx.doi.org/10.1145/103731.103733.

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22

VANIACHINE, ALEXANDRE, DAVID MALON, and MATTHEW VRANICAR. "ADVANCED TECHNOLOGIES FOR DISTRIBUTED DATABASE SERVICES HYPERINFRASTRUCTURE." International Journal of Modern Physics A 20, no. 16 (June 30, 2005): 3877–79. http://dx.doi.org/10.1142/s0217751x05027862.

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HEP collaborations are deploying grid technologies to address petabyte-scale data processing challenges. In addition to file-based event data, HEP data processing requires access to terabytes of non-event data (detector conditions, calibrations, etc.) stored in relational databases. Inadequate for non-event data delivery in these amounts, database access control technologies for grid computing are limited to encrypted message transfers. To overcome these database access limitations one must go beyond the existing grid infrastructure. A proposed hyperinfrastructure of distributed database services implements efficient secure data access methods. We introduce several technologies laying a foundation of a new hyperinfrastructure. We present efficient secure data transfer methods and secure grid query engine technologies federating heterogeneous databases. Lessons learned in a production environment of ATLAS Data Challenges are presented.

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23

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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24

Ahmad, Khaleel, Mohammad Shoaib Alam, and Nur Izura Udzir. "Security of NoSQL Database Against Intruders." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 5–12. http://dx.doi.org/10.2174/1872212112666180731114714.

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Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.

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25

Hsu, Ing-Miin, Mukesh Singhal, and Ming T. Liu. "Distributed Rule Monitoring in Active Databases." Integrated Computer-Aided Engineering 1, no. 4 (March 1, 1994): 295–310. http://dx.doi.org/10.3233/ica-1994-1403.

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26

Saran, S., P. Singh, K. N. Ganeshaiah, H. Padalia, and K. Oberoi. "INTEROPERABLE MODEL FOR BIORESOURCE DISTRIBUTED DATABASES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 791–800. http://dx.doi.org/10.5194/isprs-archives-xlii-5-791-2018.

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<p><strong>Abstract.</strong> Recently, numerous frameworks and tools are being developed for enhancing access to data and services with a standardized view to communicate the advances in open information sharing. Another emerging field of data exploration is encountered in the coordination, examination and perception of bioresource data and are prompting corresponding new innovations. The bioresource information team aims to develop standards for nationwide data exchange by the establishment of a catalog service to locate and access biological data and information from across the country and information tool for decision makers. With the growth of open data sharing initiatives, the sharing of data among different and myriad sources has increased significantly, but major challenge lies in addressing the issues of interoperability during exchange and use since the data sources are heterogeneous and the data being organization specific is prepared with different (organization) specific data standards and platforms. This paper presents the model based on the study of different metadata standards and to develop a recommended standard for biodiversity information to support interoperability among heterogeneous databases under the umbrella of Indian Bioresource Information Network (IBIN) portal. The paper presents the mapping of different data standards into the IBIN standard for sharing species data in the form of distributed and interoperable web services to set the stage for interoperability.</p>

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27

Nejdl, W., S. Ceri, and G. Wiederhold. "Evaluating recursive queries in distributed databases." IEEE Transactions on Knowledge and Data Engineering 5, no. 1 (1993): 104–21. http://dx.doi.org/10.1109/69.204095.

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28

Chiu, Ruey‐Kei, S. C. Lenny Koh, and Chi‐Ming Chang. "The aggregation for enterprise distributed databases." Journal of Manufacturing Technology Management 18, no. 7 (September 11, 2007): 889–903. http://dx.doi.org/10.1108/17410380710817318.

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29

Garcia-Molina, H., and B. Kogan. "Achieving high availability in distributed databases." IEEE Transactions on Software Engineering 14, no. 7 (July 1988): 886–96. http://dx.doi.org/10.1109/32.42732.

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30

Pramanik, S., and D. Vineyard. "Optimizing join queries in distributed databases." IEEE Transactions on Software Engineering 14, no. 9 (1988): 1319–26. http://dx.doi.org/10.1109/32.6175.

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31

Worboys, M. F., and S. M. Deen. "Semantic heterogeneity in distributed geographic databases." ACM SIGMOD Record 20, no. 4 (December 1991): 30–34. http://dx.doi.org/10.1145/141356.141366.

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32

Aiken, J. A., S. T. Parker, and D. R. Woodwell. "Achieving interoperability with distributed relational databases." IEEE Network 5, no. 1 (January 1991): 38–45. http://dx.doi.org/10.1109/65.67858.

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33

Zhang, Ji, Xiaohui Tao, and Hua Wang. "Outlier detection from large distributed databases." World Wide Web 17, no. 4 (May 1, 2013): 539–68. http://dx.doi.org/10.1007/s11280-013-0218-4.

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34

Costa, Rogério Luís de Carvalho, and Pedro Furtado. "Quality of experience in distributed databases." Distributed and Parallel Databases 29, no. 5-6 (May 11, 2011): 361–96. http://dx.doi.org/10.1007/s10619-011-7083-x.

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35

Hueni, A., T. Malthus, M. Kneubuehler, and M. Schaepman. "Data exchange between distributed spectral databases." Computers & Geosciences 37, no. 7 (July 2011): 861–73. http://dx.doi.org/10.1016/j.cageo.2010.12.009.

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36

Uzhastov, I. A. "Computer-aided design of distributed databases." Mathematics and Computers in Simulation 33, no. 4 (December 1991): 331–38. http://dx.doi.org/10.1016/0378-4754(91)90113-h.

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37

Falchi, Fabrizio, Claudio Gennaro, Fausto Rabitti, and Pavel Zezula. "Distance browsing in distributed multimedia databases." Future Generation Computer Systems 25, no. 1 (January 2009): 64–76. http://dx.doi.org/10.1016/j.future.2008.02.007.

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38

Drenick, P. E., and E. J. Smith. "Stochastic query optimization in distributed databases." ACM Transactions on Database Systems 18, no. 2 (June 1993): 262–88. http://dx.doi.org/10.1145/151634.151637.

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39

Cummings, Stuart W. "Distributed Databases for Clinical Data Processing." Drug Information Journal 27, no. 4 (October 1993): 949–56. http://dx.doi.org/10.1177/009286159302700403.

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40

Badia, Antonio, and Michael Dobbs. "Supporting quantified queries in distributed databases." International Journal of Parallel, Emergent and Distributed Systems 29, no. 5 (April 7, 2014): 421–59. http://dx.doi.org/10.1080/17445760.2014.894513.

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41

Tiwari, Sanjai, and H. Craig Howard. "Distributed AEC databases for collaborative design." Engineering with Computers 10, no. 3 (September 1994): 140–54. http://dx.doi.org/10.1007/bf01198740.

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42

Zhang, Shuai, Sally I. McClean, and Bryan W. Scotney. "Clustering semantically heterogeneous distributed aggregate databases." Knowledge and Information Systems 38, no. 2 (December 22, 2012): 331–64. http://dx.doi.org/10.1007/s10115-012-0588-4.

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43

Schall, Eric. "Parallel cycle detection in distributed databases." Information Systems 15, no. 5 (January 1990): 555–66. http://dx.doi.org/10.1016/0306-4379(90)90028-n.

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44

Ravve, Elena V. "Incremental computations over strongly distributed databases." Concurrency and Computation: Practice and Experience 28, no. 11 (October 16, 2015): 3061–76. http://dx.doi.org/10.1002/cpe.3597.

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45

Alonso, G., and A. El Abbadi. "Partitioned data objects in distributed databases." Distributed and Parallel Databases 3, no. 1 (January 1995): 5–35. http://dx.doi.org/10.1007/bf01263655.

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46

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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47

Muley, Abhinav. "Global Data Fusion versus Local Pattern Fusion in Mining Multiple Databases: A Comparative Review." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3844–49. http://dx.doi.org/10.1166/jctn.2020.9046.

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With the emergence of big data, mining distributed databases has become a critical task in the domain of discovery of knowledge from databases. Many of the traditional multiple-database mining methods developed until now have emphasized mining the mono-database, which is a pool of all the local databases merged at a central site; local patterns discovered at local sites are not analyzed in mono-database mining. However, in real-world applications, data collected from multiple databases may be duplicitous and unreliable. Therefore, developing methods to discover reliable, high-quality knowledge from multiple databases is a challenging task when mining multi-sourced data. This paper scrupulously reviews all the existing methods for mining multiple and distributed databases based on global data fusion and local pattern fusion techniques. The research issues and recently developed methods, which involves local pattern analysis in multi-database mining, are also discussed.

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48

Lutfina, Erba, Ahmad Nugroho, and Muhammad Zakki Abdillah. "Systematic Literature Review Untuk Identifikasi Metodde Fragmentasi Pada Perancangan Sistem Database Terdistribusi." Science Technology and Management Journal 2, no. 2 (August 31, 2022): 56–61. http://dx.doi.org/10.53416/stmj.v2i2.93.

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Large and complex databases that exist on a system can result in poor performance such as increasing access time and costs on a system. There are various ways to reduce the time and cost of data access and improve the performance of a database, one of them is by designing a Distributed Database. It is not known how many methods can be used to design a distributed database. This study aims to identify a distributed database design method using the Systematic Literature Review (SLR) method whose data were obtained from related journals from 2012–2022. This review is helpful for researchers as well as practitioners, for researchers this research will help them to see trends in the field of vertical fragmentation in distributed databases.

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49

Jing, Changhong, Wenjie Liu, Jintao Gao, and Ouya Pei. "Research and implementation of HTAP for distributed database." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 2 (April 2021): 430–38. http://dx.doi.org/10.1051/jnwpu/20213920430.

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Data processing can be roughly divided into two categories, online transaction processing OLTP(on-line transaction processing) and online analytical processing OLAP(on-line analytical processing). OLTP is the main application of traditional relational databases, and it is some basic daily transaction processing, such as bank pipeline transactions and so on. OLAP is the main application of the data warehouse system, it supports some more complex data analysis operations, focuses on decision support, and provides popular and intuitive analysis results. As the amount of data processed by enterprises continues to increase, distributed databases have gradually replaced stand-alone databases and become the mainstream of applications. However, the current business supported by distributed databases is mainly based on OLTP applications, lacking OLAP implementation. This paper proposes an implementation method of HTAP for distributed database CBase, which provides an implementation method of OLAP analysis for CBase, and can easily deal with data analysis of large amounts of data.

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50

Mai, Nga Thúy. "HEURISTIC ALGORITHM FOR FRAGMENTATION AND ALLOCATION IN DISTRIBUTED OBJECT ORIENTED DATABASE." Journal of Computer Science and Cybernetics 32, no. 1 (April 8, 2016): 47–60. http://dx.doi.org/10.15625/1813-9663/32/1/5772.

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Abstract:

Class fragmentation and allocation is an important technique to improve the performance of a distributed object oriented database system. The class fragmentation is to split a class into smaller pieces in distributed databases aims to reduce the access to unnecessary data, the allocation is to locate fragmented classes into the sites in the connected network properly to reduce the cost of data transmission. Classes in object databases include attributes describing the characteristics of the object, methods describing the behavior, and relationships with objects with other classes, including relations inheritance. With such characteristics, class fragmentation and allocation in the distributed object oriented database system is more complex than fragmentation technique and design of relational databases. Fragmentation techniques applied in the design of distributed object-oriented database today often do not use cost between the sites, fragments are allocated to the site after getting a fragmentation method of data objects. This paper proposes an algorithm of fragmentation and allocation simultaneously, including the cost of data communication between the sites used for fragmentation to reduce communication costs when processing and querying distributed data.

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