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Quasi-SLCA Based Keyword QueryProcessing over Probabilistic XML Data
April 2014 (vol. 26 no. 4)
pp. 957-969
Jeffrey Xu Yu, Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Hong Kong, China
Rui Zhou, Fac. of Inf. & Technol., Swinburne Univ. of Technol.-Hawthorn Campus, Hawthorn, VIC, Australia
Chengfei Liu, Fac. of Inf. & Technol., Swinburne Univ. of Technol.-Hawthorn Campus, Hawthorn, VIC, Australia
Jianxin Li, Fac. of Inf. & Technol., Swinburne Univ. of Technol.-Hawthorn Campus, Hawthorn, VIC, Australia
The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ)over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI)index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches.
Index Terms:
probabilistic index,Probabilistic XML,threshold keyword query
Citation:
Jeffrey Xu Yu, Rui Zhou, Chengfei Liu, Jianxin Li, "Quasi-SLCA Based Keyword QueryProcessing over Probabilistic XML Data," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 4, pp. 957-969, April 2014, doi:10.1109/TKDE.2013.67
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