Compressing and Searching XML Data Via Two Zips

XML is fast becoming the standard format to store, exchange and publish over the web, and is getting embedded in applications. Two challenges in handling XML are its size (the XML representation of a document is significantly larger than its native state) and the complexity of its search (XML search involves path and content searches on labeled tree structures). We address the basic problems of compression, navigation and searching of XML documents. In particular, we adopt recently proposed theoretical algorithms for succinct tree representations to design and implement a compressed index for XML, called XBzipIndex, in which the XML document is maintained in a highly compressed format, and both navigation and searching can be done uncompressing only a tiny fraction of the data. This solution relies on compressing and indexing two arrays derived from the XML data. With detailed experiments we compare this with other compressed XML indexing and searching engines to show that XBzipIndex has compression ratio up to 35% better than the ones achievable by those other tools, and its time performance on some path and content search operations is order of magnitudes faster: few milliseconds over hundreds of MBs of XML files versus tens of seconds, on standard XML data sources.