Issue No. 04 - April (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.67
Jianxin Li , 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
Rui Zhou , Fac. of Inf. & Technol., Swinburne Univ. of Technol.-Hawthorn Campus, Hawthorn, VIC, Australia
Jeffrey Xu Yu , Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Hong Kong, China
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.
Probabilistic logic, XML, Semantics, Indexes, Upper bound, Probability, Data models
Jianxin Li, Chengfei Liu, Rui Zhou and J. X. Yu, "Quasi-SLCA Based Keyword Query Processing over Probabilistic XML Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 4, pp. 957-969, 2014.