1. Hierarchical simultaneous vertical fragmentation and allocation using modified Bond Energy Algorithm in distributed databases
- Author
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Davoud Riahi, Hossein Rahimi, and Fereshteh-Azadi Parand
- Subjects
Correctness ,Computer science ,Distributed computing ,02 engineering and technology ,computer.software_genre ,Clustering ,Bond Energy Algorithm ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Cluster (physics) ,Bond energy ,Cluster analysis ,GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries) ,ComputingMilieux_MISCELLANEOUS ,Data allocation and fragmentation ,lcsh:T58.5-58.64 ,Distributed database ,lcsh:Information technology ,Fragmentation (computing) ,Distributed database system ,Computer Science Applications ,Important research ,020201 artificial intelligence & image processing ,Data mining ,computer ,Algorithm ,Software ,Information Systems - Abstract
Designing an efficient Distributed Database System (DDBS) is considered as one of the most challenging problems because of multiple interdependent factors which are affecting its performance. Allocation and fragmentation are two processes which their efficiency and correctness influence the performance of DDBS. Therefore, efficient data fragmentation and allocation of fragments across the network sites are considered as an important research area in distributed database design. This paper presents an approach which simultaneously fragments data vertically and allocates the fragments to appropriate sites across the network. Bond Energy Algorithm (BEA) is applied with a better affinity measure that improves the generated clusters of attributes. The algorithm simultaneously generates clusters of attributes, calculates the cost of allocating each cluster to each site and allocates each cluster to the most appropriate site. Results show more efficient clustering and allocation which gives better performance. Keywords: Bond Energy Algorithm, Distributed database system, Data allocation and fragmentation, Clustering
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