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Information 2019, 10(3), 112; https://doi.org/10.3390/info10030112

Towards an Efficient Data Fragmentation, Allocation, and Clustering Approach in a Distributed Environment

1
College of Technological Innovation, Zayed University, P.O. Box 144534, Abu Dhabi 11543, UAE
2
College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 27 December 2018 / Revised: 28 January 2019 / Accepted: 11 February 2019 / Published: 12 March 2019
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Abstract

Data fragmentation and allocation has for long proven to be an efficient technique for improving the performance of distributed database systems’ (DDBSs). A crucial feature of any successful DDBS design revolves around placing an intrinsic emphasis on minimizing transmission costs (TC). This work; therefore, focuses on improving distribution performance based on transmission cost minimization. To do so, data fragmentation and allocation techniques are utilized in this work along with investigating several data replication scenarios. Moreover, site clustering is leveraged with the aim of producing a minimum possible number of highly balanced clusters. By doing so, TC is proved to be immensely reduced, as depicted in performance evaluation. DDBS performance is measured using TC objective function. An inclusive evaluation has been made in a simulated environment, and the compared results have demonstrated the superiority and efficacy of the proposed approach on reducing TC. View Full-Text
Keywords: vertical fragmentation; clustering; data allocation; data replication; site clustering; DDBS vertical fragmentation; clustering; data allocation; data replication; site clustering; DDBS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Abdalla, H.; Artoli, A.M. Towards an Efficient Data Fragmentation, Allocation, and Clustering Approach in a Distributed Environment. Information 2019, 10, 112.

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