Abstract
AbstractThe relational database machine GRACE can execute heavy relational operations such as joins quite efficiently, thereby resolving the bottleneck in the relational database processing. A new bottleneck, however, is expected to appear at accesses to the secondary storage system. We can also eliminate this new bottleneck by partitioning the database into multidimensional cells adaptively to the access pattern to data. As a result, the average number of page accesses is reduced. We developed an adaptive multidimensional clustering technique called the generalized KD‐tree method. The method is fully adaptive to the access pattern to data and the distribution of tuples. It was shown that the generalized KD‐tree method can reduce the average number of page accesses considerably, even in comparison with other multidimensional clustering algorithms.
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