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Traditional indexing techniques commonly employed in da\-ta\-ba\-se systemsperform poorly on multidimensional array scientific data. Bitmap indices arewidely used in commercial databases for processing complex queries, due totheir effective use of bit-wise operations and space-efficiency. However,bitmap indices apply natively to relational or linearized datasets, which isespecially notable in binned or compressed indices.  We propose a new method for multidimensional array indexing that overcomesthe dimensionality-induced inefficiencies. The hierarchical indexing method isbased on $n$-di\-men\-sional sparse trees for dimension partitioning, withbound number of individual, adaptively binned indices for attributepartitioning. This indexing performs well on range involving both dimensionsand attributes, as it prunes the search space early, avoids reading entireindex data, and does at most a single index traversal. Moreover, the indexingis easily extensible to membership queries.  The indexing method was implemented on top of a state of the art bitmapindexing library Fastbit. We show that the hierarchical bitmap indexoutperforms conventional bitmap indexing built on auxiliary attribute for eachdimension. Furthermore, the adaptive binning significantly reduces the amountof bins and therefore memory requirements.

Hierarchical Bitmap Indexing for Range and Membership Queries on Multidimensional Arrays