Design and implementation of bitmap indices for scientific data

Bitmap indices are efficient multi-dimensional index data structures for handling complex adhoc queries in read-mostly environments. They have been implemented in several commercial database systems but are only well suited for discrete attribute values which are very common in typical business applications. However, many scientific applications usually operate on floating point numbers and cannot take advantage of the optimisation techniques offered by current database solutions. We thus present a novel algorithm called GenericRangeEval for processing one-sided range queries over floating point values. In addition, we present a cost model for predicting the performance of bitmap indices for high-dimensional search spaces. We verify our analytical results by a detailed experimental study, and show that the presented bitmap evaluation algorithm scales well also for high-dimensional search spaces requiring only a fairly small index. Because of its simple arithmetic structure, the cost model could easily be integrated into a query optimiser for deciding whether the current multi-dimensional query shall be answered by means of a bitmap index or better by sequentially scanning the data values, without using an index at all.