Data space mapping for efficient I/O in large multi-dimensional databases

In this paper, we propose data space mapping techniques for storage and retrieval in multi-dimensional databases on multi-disk architectures. We identify the important factors for an efficient multi-disk searching of multi-dimensional data and develop secondary storage organization and retrieval techniques that directly address these factors. We especially focus on high dimensional data, where none of the current approaches are effective. In contrast to the current declustering techniques, storage techniques in this paper consider both inter- and intra-disk organization of the data. The data space is first partitioned into buckets, then the buckets are declustered to multiple disks while they are clustered in each disk. The queries are executed through bucket identification techniques that locate the pages. One of the partitioning techniques we discuss is especially practical for high dimensional data, and our disk and page allocation techniques are optimal with respect to number of I/O accesses and seek times. We provide experimental results that support our claims on two real high dimensional datasets.