Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/109520
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Type: | Conference paper |
Title: | Efficient computation of multiple XML keyword queries |
Author: | Yao, L. Liu, C. Li, J. Zhou, R. |
Citation: | Lecture Notes in Artificial Intelligence, 2013 / Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (ed./s), vol.8180, iss.PART 1, pp.368-381 |
Publisher: | Springer |
Issue Date: | 2013 |
Series/Report no.: | LNCS |
ISBN: | 9783642412295 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Conference on Web Information Systems Engineering (WISE) (13 Oct 2013 - 15 Oct 2013 : Nanjing, China) |
Editor: | Lin, X. Manolopoulos, Y. Srivastava, D. Huang, G. |
Statement of Responsibility: | Liang Yao, Chengfei Liu, Jianxin Li, and Rui Zhou |
Abstract: | Answering keyword queries on XML data has been extensively studied. Current XML keyword search solutions primarily focus on single query setting where queries are answered individually. In many applications for searching information such as jobs and publications, an application server often receives a large number of keyword queries in a short period of time and many of them may share common keywords. Therefore, answering keyword queries in batches will significantly enhance the performance of these applications. In this paper, we investigate efficient approaches for computing multiple XML keyword queries. We first propose an approach that maximizes the sharing among keyword queries. We then consider useful data information and propose two data-aware algorithms: a short eager algorithm and a log based optimal algorithm. We evaluate the proposed algorithms on real and synthetic datasets and the experimental results demonstrate their efficiencies. |
Rights: | © Springer-Verlag Berlin Heidelberg 2013 |
DOI: | 10.1007/978-3-642-41230-1_31 |
Grant ID: | http://purl.org/au-research/grants/arc/DP110102407 |
Published version: | http://dx.doi.org/10.1007/978-3-642-41230-1_31 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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RA_hdl_109520.pdf Restricted Access | Restricted access | 327.08 kB | Adobe PDF | View/Open |
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