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ELCA evaluation for keyword search on probabilistic XML data

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journal contribution
posted on 2024-07-09, 16:09 authored by Rui ZhouRui Zhou, Chengfei LiuChengfei Liu, Jianxin Li, Jeffrey Xu Yu
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.

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PDF (Accepted manuscript)

ISSN

1386-145X

Journal title

World Wide Web

Volume

16

Issue

2

Pagination

171-193

Publisher

Springer

Copyright statement

Copyright © Springer Science+Business Media, LLC 2012. The accepted manuscript is reproduced in accordance with the copyright policy of the publisher. The definitive version of the publication is available at www.springer.com.

Language

eng

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