Superimposed Code-Based Indexing Method for Extracting MCTs from XML Documents

  • Authors:

  • Wenxin Liang;Takeshi Miki;Haruo Yokota

  • Affiliations:

  • CREST, Japan Science and Technology Agency (JST), and Global Scientific Information and Computing Center, Tokyo Institute of Technology,;Nomura Research Institute,;Department of Computer Science, Tokyo Institute of Technology, and Global Scientific Information and Computing Center, Tokyo Institute of Technology,

  • Venue:
  • DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
  • Year:
  • 2008

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Abstract

With the exponential increase in the amount of XML data on the Internet, information retrieval techniques on tree-structured XML documents such as keyword search become important. The search results for this retrieval technique are often represented by minimum connecting trees (MCTs) rooted at the lowest common ancestors (LCAs) of the nodes containing all the search keywords. Recently, effective methods such as the stack-based algorithm for generating the lowest grouped distance MCTs (GDMCTs), which derive a more compact representation of the query results, have been proposed. However, when the XML documents and the number of search keywords become large, these methods are still expensive. To achieve more efficient algorithms for extracting MCTs, especially lowest GDMCTs, we first consider two straightforward LCA detection methods: keyword B+trees with Dewey-order labels and superimposed code-based indexing methods. Then, we propose a method for efficiently detecting the LCAs, which combines the two straightforward indexing methods for LCA detection. We also present an effective solution for the false drop problem caused by the superimposed code. Finally, the proposed LCA detection methods are applied to generate the lowest GDMCTs. We conduct detailed experiments to evaluate the benefits of our proposed algorithms and show that the proposed combined method can completely solve the false drop problem and outperforms the stack-based algorithm in extracting the lowest GDMCTs.