Advanced Object Database Design Techniques


Adrian Sergiu DarabantAlina Câmpan


Abstract

carpathian_2004_20_021_030_abstract

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carpathian_2004_20_021_030

Class fragmentation is an important task in the design of Distributed Object Oriented Databases (DOOD). However, fragmentation in DOOD is still at its beginnings and mostly adapted from the relational approaches. In this paper we propose an alternative approach for horizontal fragmentation of DOOD. Our method uses two different AI clustering techniques for partitioning class instances into fragments: the agglomerative hierarchical method and the k-means centroid based method. Class objects are modelled in a vector space; similarity be- tween objects is computed using different measures. Finally, we provide quality and performance evaluations using a partition evaluator function .

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Author(s)

Câmpan, Alina, Darabant, Adrian Sergiu