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We propose the first known solution to the problem of correlating, in small space, continuous streams of XML data through approximate (structure and content) matching, as defined by a general tree-edit distance metric. The key element of our solution is a novel algorithm for oblviously embedding tree-edit distance metrics into an L_1 vector space while guaranteeing an upper bound of O(log^2 n log* n) on the distance distortion between any data trees with at most n nodes. We demonstrate how our embedding algorithm can be applied in conjunction with known random sketching techniques to: (1) build a compact synopsis of a massive, streaming XML data tree that can be used as a concise surrogate for the full tree in approximate tree-edit distance computations; and, (2) approximate the result of tree-edit-distance similarity joins over continuous XML document streams. To the best of our knowledge, these are the first algorithmic results on low-distortion embeddings for tree-edit distance metrics, and on correlating (e.g., through similarity joins) XML data in the streaming model.
@inproceedings {DBLP:conf/pods/GarofalakisK03, author = {Minos N. Garofalakis and Amit Kumar}, booktitle = {PODS}, title = {Correlating XML data streams using tree-edit distance embeddings.}, pages = {143-154}, year = {2003}, url = {db/conf/pods/pods2003.html#GarofalakisK03}, ee = {http://doi.acm.org/10.1145/773153.773168}, crossref = {conf/pods/2003}, bibsource = {DBLP, http://dblp.uni-trier.de} }  ©2004 Association for Computing Machinery |
