A hybrid object clustering strategy for large knowledge-based systems

Object bases underlying knowledge based applications tend to be complex and require management. This research aims at improving the performance of object bases underlying a class of large knowledge based systems that utilize object oriented technology to engineer the knowledge base. A hybrid clustering strategy that beneficially combines semantic clustering and iterative graph partitioning techniques has been developed and evaluated for use in knowledge bases storing information in the form of object graphs. It is demonstrated via experimentation that such a technique is useful and feasible in realistic object bases. A semantic specification mechanism similar to placement trees has been developed for specifying the clustering. The workload and the nature of object graphs in knowledge bases differ significantly from those present in conventional object oriented databases. Therefore, the evaluation has been performed by building a new benchmark called the Granularity Benchmark. A segmented storage scheme for the knowledge base using large object storage mechanisms of existing storage managers is also examined.