Abstract
XML clustering finds many applications, ranging from storage to query processing. However, existing clustering algorithms focus on static XML collections, whereas modern information systems frequently deal with streaming XML data that needs to be processed online. Streaming XML clustering is a challenging task because of the high computational and space efficiency requirements implicated for online approaches. In this paper we propose XStreamCluster, which addresses the two challenges using a two-layered optimization. The bottom layer employs Bloom filters to encode the XML documents, providing a space-efficient solution to memory usage. The top layer is based on Locality Sensitive Hashing and contributes to the computational efficiency. The theoretical analysis shows that the approximate solution of XStreamCluster generates similarly good clusters as the exact solution, with high probability. The experimental results demonstrate that XStreamCluster improves both memory efficiency and computational time by at least an order of magnitude without affecting clustering quality, compared to its variants and a baseline approach.
This work is partially supported by the FP7 EU Project GLOCAL (contract no. 248984).
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Papapetrou, O., Chen, L. (2011). XStreamCluster: An Efficient Algorithm for Streaming XML Data Clustering. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_36
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DOI: https://doi.org/10.1007/978-3-642-20149-3_36
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