›› 2013, Vol. 28 ›› Issue (2): 366-381.doi: 10.1007/s11390-013-1337-1

Special Issue: Data Management and Data Mining

• Database and Data Management • Previous Articles     Next Articles

Fast Smallest Lowest Common Ancestor Computation Based on Stable Match

Jun-Feng Zhou1,3 (周军锋), Member, CCF, Guo-Xiang Lan1 (蓝国翔), Zi-Yang Chen1 (陈子阳), Member, CCF and Xian Tang2 (汤显)   

  1. 1 School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;
    2 School of Economics and Management, Yanshan University, Qinhuangdao 066004, China;
    3 Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan UniversityQinhuangdao 066004, China
  • Received:2011-11-11 Revised:2012-05-30 Online:2013-03-05 Published:2013-03-05
  • Supported by:

    This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61073060, 61040023, 61272124, and the Science and Technology Research and Development Program of Hebei Province of China under Grant No. 11213578.

In this paper, we focus on efficient processing of XML keyword queries based on smallest lowest common ancestor (SLCA) semantics. For a given query Q with m keywords, we propose to use stable matches as the basis for SLCA computation, where each stable match M consists of m nodes that belong to the m distinct keyword inverted lists of Q. M satisfies that no other lowest common ancestor (LCA) node of Q can be found to be located after the first node of M and be a descendant of the LCA of M, based on which the operation of locating a stable match can skip more useless nodes. We propose two stable match based algorithms for SLCA computation, i.e., BSLCA and HSLCA. BSLCA processes two keyword inverted lists each time from the shortest to the longest, while HSLCA processes all keyword inverted lists in a holistic way to avoid the problem of redundant computation invoked by BSLCA. Our extensive experimental results verify the performance advantages of our methods according to various evaluation metrics.

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