Approximate Structural Matching over Ordered XML Documents

Abstract

There is an increasing need for an XML query engine that not only searches for exact matches to a query but also returns "query-like" structures. We have designed and developed XFinder, an efficient top K tree pattern query evaluation system, which reduces the problem of approximate tree structural matching to a simpler problem of subsequence matching. However, since not all subsequences correspond to valid tree structures, it is expensive to enumerate common subsequences between XML data and query and then filter the invalid ones. XFinder addresses this challenge by detecting and pruning structurally irrelevant subsequence matches as early as possible. Experiments show the efficiency of XFinder on various data and query sets.

TASM: Top-k Approximate Subtree Matching

“…The edit model used to compute distances in XFinder does not handle renaming operations. Also, in [6] no runtime analysis is given and the experiments reported use documents of up to 5MB. In contrast, we provide and validate tight analytical bounds, solve the problem with the unrestricted tree edit distance and efficiently apply our solution to documents of 1.6GB.…”

“…XFinder [6] ranks the top-k approximate matches of a small query tree in a large document tree. Both the query and the document are transformed to strings using Prüfer sequences, and the tree edit distance is approximated by the longest subsequence distance between the resulting strings.…”

“…Along with this development has come the need for efficient techniques to approximately match XML trees based on their similarity according to a given distance metric. Approximate matching is used for integrating heterogeneous repositories [1], [2], [3], [4], cleaning such integrated data [5], as well as for answering similarity queries [6], [7]. In this paper we consider the Top-k Approximate Subtree Matching problem (TASM), i.e., the problem of ranking the k best approximate matches of a small query tree in a large document tree.…”

An efficient method for extracting subtrees against forest query

“…In this section, we will discuss how to extract Top-k best matched subtree from a tree database. Augsten et al proposed an algorithm to compute top-k subtree [10]and Agarwal et al [6] presented an algorithm which uses which establishes a oneto-one correspondence between trees and sequences. In this work, we have used the algorithm proposed by Augsten et al as a subroutine of our algorithm [10].…”

“…In many of the cases finding exact matching is not possible but approximate matching can serve the purpose. Thus, an efficient algorithm for finding approximate matching is important [6]. State-of-the-art algorithms can find top-k subtrees from tree database in polynomial time [1,6], but handling query as a forest is still an issue that haven't been addressed yet.…”