performances of clustering policies in object bases
In this paper, we address the problem of clustering graphs in object-oriented databases. Unlike previous studies which focused only on a workload consisting of a single operation, this study tackles the problem when the workload is a set of operations (method and queries) that occur with a certain probability. Thus, the goal is to minimize the expected cost of an operation in the workload, while maintaining a similarly low cost for each individual operation class. To this end, we present a new clustering policy based on the nearest-neighbor graph partitioning algorithm. We then demonstrate that this policy provides considerable gains when compared to a suite of well-known clustering policies proposed in the literature. Our results are based on two widely referenced object-oriented database benchmarks; namely, the Tektronix HyperModel and OO7.
graph partitioning performance analysis storage techniques object-oriented database systems