Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/109525
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Type: Journal article
Title: ELCA evaluation for keyword search on probabilistic XML data
Author: Zhou, R.
Liu, C.
Li, J.
Yu, J.
Citation: World Wide Web, 2013; 16(2):171-193
Publisher: Taylor & Francis
Issue Date: 2013
ISSN: 1386-145X
1573-1413
Statement of
Responsibility: 
Rui Zhou, Chengfei Liu, Jianxin Li, Jeffrey Xu Yu
Abstract: As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result probability, time and space efficiency, and scalability.
Keywords: ELCA; probabilistic; XML; keyword search; keyword query; uncertain
Rights: © Springer Science+Business Media, LLC 2012
DOI: 10.1007/s11280-012-0166-4
Grant ID: http://purl.org/au-research/grants/arc/DP110102407
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