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Type: Journal article
Title: Quasi-SLCA based keyword query processing over probabilistic XML data
Author: Li, J.
Liu, C.
Zhou, R.
Yu, J.
Citation: IEEE Transactions on Knowledge and Data Engineering, 2014; 26(4):957-969
Publisher: IEEE
Issue Date: 2014
ISSN: 1041-4347
Statement of
Jianxin Li, Chengfei Liu, Rui Zhou and Jeffrey Xu Yu
Abstract: 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.
Keywords: Probabilistic XML; threshold keyword query; probabilistic index
Rights: © 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TKDE.2013.67
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