A storage scheme for multidimensional data alleviating dimension dependency

Abstract

Multidimensional arrays storing multidimensional data in MOLAP are usually very sparse. They also suffer from the problem that the time consumed in sequential access to array elements heavily depends on the dimension along which the elements are accessed. This problem of ldquodimension dependencyrdquo would be alleviated by dividing the whole array into the set of smaller hypercube shaped subarrays called ldquochunksrdquo. But the chunks are also sparse and should be compressed. However, further dimension dependency in accessing array elements would be caused, unless these compressed chunks are arranged judiciously in the page buffer. The difference among the dimension cardinalities could also cause dimension dependency; slice operation along a dimension of large cardinality tends to consume much time. We will alleviate these two kinds of dimension dependency by introducing the notion of an ldquoextended chunkrdquo. Extended chunks can adapt flexibly to the general situation where data densities in chunks are low and are not uniformly distributed. Employing extended chunks, we will propose some secondary storage schemes for a multidimensional array using a space-filling curve such as Z-curve. The evaluation result shows that the proposed storage schemes exhibit good performance while alleviating the dimension dependency.