Efficient Query Evaluation over Compressed XML Data

XML suffers from the major limitation of high redundancy. Even if compression can be beneficial for XML data, however, once compressed, the data can be seldom browsed and queried in an efficient way. To address this problem, we propose XQueC, an [XQue]ry processor and [C]ompressor, which covers a large set of XQuery queries in the compressed domain. We shred compressed XML into suitable data structures, aiming at both reducing memory usage at query time and querying data while compressed. XQueC is the first system to take advantage of a query workload to choose the compression algorithms, and to group the compressed data granules according to their common properties. By means of experiments, we show that good trade-offs between compression ratio and query capability can be achieved in several real cases, as those covered by an XML benchmark. On average, XQueC improves over previous XML query-aware compression systems, still being reasonably closer to general-purpose query-unaware XML compressors. Finally, QETs for a wide variety of queries show that XQueC can reach speed comparable to XQuery engines on uncompressed data.