AI Clustering Techniques: a New Approach to Object Oriented Database Fragmentation

Optimal application performance on a Distributed Object Based System requires class fragmentation and the development of allocation schemes to place fragments at distributed sites so data transfer is minimal. In this paper we present a horizontal fragmentation approach that uses the k-means centroid based clustering method for partitioning object instances into fragments. Our new method takes full advantage of existing data, where statistics are already present. We model fragmentation input data in a vector space and give different object similarity measures together with their geometrical interpretations. We provide quality and performance evaluations using a partition evaluator function.