CasAB: Building Precise Bitmap Indices via Cascaded Bloom Filters

Abstract

Bitmap indices are widely used in massive and read-mostly datasets such as data warehouses and scientific databases. Recently, Bloom filters were used to encode bitmap indices into approximate bitmaps(AB). The salient advantage of this technique is that bitmaps can be directly accessed without decompression, and the query time is proportional in the size of the region being queried. This technique, however, introduces false positives due to the nature of Bloom filters, therefore, only approximate query results can be achieved. To eliminate false positives, we proposed a novel bitmap index encoding scheme, namely cascaded approximate bitmaps(CasAB) based on multi-level Bloom filter cascading, which can achieve precise query results at the cost of slightly more space and time overhead. An efficient CasAB construction algorithm and a query algorithm are given. Space and time complexities of CasAB are analyzed theoretically, and the minimum space size can be pre-computed based on the cardinality of the attribute. Experiments show that the query precision of CasAB is always 100% and space and time overhead is similar to that of AB.