Correlating XML data streams using tree-edit distance embeddings

  • Authors:

  • Minos Garofalakis;Amit Kumar

  • Affiliations:

  • Lucent Technologies, Murray Hill, NJ;Lucent Technologies, Murray Hill, NJ

  • Venue:
  • Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
  • Year:
  • 2003

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Abstract

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 obliviously embedding tree-edit distance metrics into an L1 vector space while guaranteeing an upper bound of O(log2 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.