Analyses of multi-level and multi-component compressed bitmap indexes

  • Authors:

  • Kesheng Wu;Arie Shoshani;Kurt Stockinger

  • Affiliations:

  • Lawrence Berkeley National Laboratory, Berkeley, CA;Lawrence Berkeley National Laboratory, Berkeley, CA;Lawrence Berkeley National Laboratory, Berkeley, CA

  • Venue:
  • ACM Transactions on Database Systems (TODS)
  • Year:
  • 2008

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Abstract

Bitmap indexes are known as the most effective indexing methods for range queries on append-only data, and many different bitmap indexes have been proposed in the research literature. However, only two of the simplest ones are used in commercial products. To better understand the benefits offered by the more sophisticated variations, we conduct an analytical comparison of well-known bitmap indexes, most of which are in the class of multi-component bitmap indexes. Our analysis is the first to fully incorporate the effects of compression on their performance. We produce closed-form formulas for both the index sizes and the query processing costs for the worst cases. One surprising finding is that the two simple indexes are in fact the best among multi-component indexes. Additionally, we investigate a number of novel variations in a class of multi-level indexes, and find that they answer queries faster than the best of multi-component indexes. More specifically, some two-level indexes are predicted by analyses and verified with experiments to be 5 to 10 times faster than well-known indexes. Furthermore, these two-level indexes have the optimal computational complexity for answering queries.