The Similarity Measures and their Impact on OODB Fragmentation Using Hierarchical Clustering Algorithms
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BibTeX
@MISC{Darabant_thesimilarity,
author = {Adrian Sergiu Darabant and Horea Todoran and Octavian Cret and George Chis},
title = {The Similarity Measures and their Impact on OODB Fragmentation Using Hierarchical Clustering Algorithms},
year = {}
}
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Abstract
Abstract:- Class fragmentation is an essential phase in the design of Distributed Object Oriented Databases (DOODB). Due to their semantic similarity with the purpose of database fragmentation (obtaining sets of similar objects with respect to the user applications running in the system), clustering algorithms have recently begun to be investigated in the process of database fragmentation. This work proposes a study on the impact of different similarity measures applied in hierarchical agglomerative clustering algorithms for horizontal fragmentation of classes with complex attributes. This study would eventually help finding formal, automatic, approaches in choosing a particular similarity measure in accordance with: the applied clustering algorithm, the structure of the database inheritance/aggregation hierarchies, the semantics of data, etc. Key-Words:- Distributed database design, horizontal fragmentation, data mining methods, performance evaluation. As opposed to centralized databases where the design phase handles only logical and physical data modeling, the design process in Distributed Object Oriented Databases involves both data partitioning
