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Quasi-SLCA based keyword query processing over probabilistic XML data
journal contribution
posted on 2014-04-01, 00:00 authored by Jianxin LiJianxin Li, Chengfei Liu, Rui Zhou, Jeffrey Xu YuThe 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.
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Journal
IEEE transactions on knowledge and data engineeringVolume
26Issue
4Pagination
957 - 969Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
1041-4347Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2013, IEEEUsage metrics
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