Hierarchical Bitmap Index: An Efficient and Scalable Indexing Technique for Set-Valued Attributes

Set-valued attributes are convenient to model complex objects occurring in the real world. Currently available database systems support the storage of set-valued attributes in relational tables but contain no primitives to query them efficiently. Queries involving set-valued attributes either perform full scans of the source data or make multiple passes over single-value indexes to reduce the number of retrieved tuples. Existing techniques for indexing set-valued attributes (e.g., inverted files, signature indexes or RD-trees) are not efficient enough to support fast access of set-valued data in very large databases. In this paper we present the hierarchical bitmap index—a novel technique for indexing set-valued attributes. Our index permits to index sets of arbitrary length and its performance is not affected by the size of the indexed domain. The hierarchical bitmap index efficiently supports different classes of queries, including subset, superset and similarity queries. Our experiments show that the hierarchical bitmap index outperforms other set indexing techniques significantly.