A sampling approach for XML query selectivity estimation

As the Extensible Markup Language (XML) rapidly estab- lishes itself as the de facto standard for presenting, stor- ing, and exchanging data on the Internet, large volume of XML data and their supporting facilities start to surface. A fast and accurate selectivity estimation mechanism is of practical importance because selectivity estimation plays a fundamental role in XML query optimization. Recently pro- posed techniques are all based on some forms of structure synopses that could be time-consuming to build and not ef- fective for summarizing complex structure relationships. In this research, we propose an innovative sampling method that can capture the tree structures and intricate relation- ships among nodes in a simple and eectiv e way. The de- rived sample tree is stored as a synopsis for selectivity esti- mation. Extensive experimental results show that, in com- parison with the state-of-the-art structure synopses, specif- ically the TreeSketch and Xseed synopses, our sample tree synopsis applies to a broader range of query types, requires several orders of magnitude less construction time, and gen- erates estimates with considerably better precision for com- plex datasets.