Abstract
Modern large-scale applications are generating staggering amounts of data. In an effort to summarize and index these data sets, databases often use bitmap indices. These indices have become widely adopted due to their dual properties of (1) being able to leverage fast bit-wise operations for query processing and (2) compressibility. Today, two pervasive bitmap compression schemes employ a variation of run-length encoding, aligned over bytes (BBC) and words (WAH), respectively. While BBC typically offers high compression ratios, WAH can achieve faster query processing, but often at the cost of space. Recent work has further shown that reordering the rows of a bitmap can dramatically increase compression. However, these sorted bitmaps often display patterns of changing run-lengths that are not optimal for a byte nor a word alignment. We present a general framework to facilitate a variable length compression scheme. Given a bitmap, our algorithm is able to use different encoding lengths for compression on a per-column basis. We further present an algorithm that efficiently processes queries when encoding lengths share a common integer factor. Our empirical study shows that in the best case our approach can out-compress BBC by 30% and WAH by 70%, for real data sets. Furthermore, we report a query processing speedup of 1.6× over BBC and 1.25× over WAH. We will also show that these numbers drastically improve in our synthetic, uncorrelated data sets.