SAQR: An efficient scheme for similarity-aware query refinement

Albarrak, Abdullah, Sharaf. Mohamed A. and Zhou, Xiaofang (2014). SAQR: An efficient scheme for similarity-aware query refinement. In: Sourav S. Bhowmick, Curtis E. Dyreson, Christian S. Jensen, Mong Li Lee, Agus Muliantara and Bernhard Thalheim, Database Systems for Advanced Applications - 19th International Conference, DASFAA 2014, Proceedings. 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014, Bali, Indonesia, (110-125). 21 - 24 April 2014. doi:10.1007/978-3-319-05810-8_8


Author Albarrak, Abdullah
Sharaf. Mohamed A.
Zhou, Xiaofang
Title of paper SAQR: An efficient scheme for similarity-aware query refinement
Conference name 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014
Conference location Bali, Indonesia
Conference dates 21 - 24 April 2014
Proceedings title Database Systems for Advanced Applications - 19th International Conference, DASFAA 2014, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Series Lecture Notes in Computer Science
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1007/978-3-319-05810-8_8
ISBN 9783319058092
9783319058108
ISSN 0302-9743
1611-3349
Editor Sourav S. Bhowmick
Curtis E. Dyreson
Christian S. Jensen
Mong Li Lee
Agus Muliantara
Bernhard Thalheim
Volume 8421 LNCS
Issue PART 1
Start page 110
End page 125
Total pages 16
Chapter number 8
Total chapters 33
Collection year 2015
Language eng
Abstract/Summary Query refinement techniques enable database systems to automatically adjust a submitted query so that its result satisfies some specified constraints. While current techniques are fairly successful in generating refined queries based on cardinality constraints, they are rather oblivious to the (dis)similarity between the input query and its corresponding refined version. Meanwhile, enforcing a similarity-aware query refinement is a rather challenging task as it would require an exhaustive examination of the large space of possible query refinements. To address this challenge, we propose a novel scheme for efficient Similarity-aware Query Refinement (SAQR). SAQR aims to balance the tradeoff between satisfying the cardinality and similarity constraints imposed on the refined query so that to maximize its overall benefit to the user. To achieve that goal, SAQR implements efficient strategies to minimize the costs incurred in exploring the available search space. In particular, SAQR utilizes both similarity-based and cardinality-based pruning techniques to bound the search space and quickly find a refined query that meets the user expectations. Our experimental evaluation shows the scalability exhibited by SAQR under various workload settings, and the significant benefits it provides.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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