Top-k keyword search over probabilistic XML data

  • Authors:

  • Jianxin Li;Chengfei Liu;Rui Zhou;Wei Wang

  • Affiliations:

  • Swinburne University of Technology, Australia;Swinburne University of Technology, Australia;Swinburne University of Technology, Australia;University of New South Wales, Australia

  • Venue:
  • ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
  • Year:
  • 2011

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Abstract

Despite the proliferation of work on XML keyword query, it remains open to support keyword query over probabilistic XML data. Compared with traditional keyword search, it is far more expensive to answer a keyword query over probabilistic XML data due to the consideration of possible world semantics. In this paper, we firstly define the new problem of studying top-k keyword search over probabilistic XML data, which is to retrieve k SLCA results with the k highest probabilities of existence. And then we propose two efficient algorithms. The first algorithm PrStack can find k SLCA results with the k highest probabilities by scanning the relevant keyword nodes only once. To further improve the efficiency, we propose a second algorithm EagerTopK based on a set of pruning properties which can quickly prune unsatisfied SLCA candidates. Finally, we implement the two algorithms and compare their performance with analysis of extensive experimental results.