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2017 IEEE 33rd International Conference on Data Engineering (2017)
San Diego, California, USA
April 19, 2017 to April 22, 2017
ISSN: 2375-026X
ISBN: 978-1-5090-6543-1
pp: 107-110
With the continued proliferation of location-based services, a growing number of web-accessible data objects are geotagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-aware why-not spatial keyword top-k query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users' directionaware queries. Experimental studies demonstrate the efficiency and effectiveness of the proposed techniques.
Search problems, Legged locomotion, Computer science, Spatial databases, Computational modeling, Q measurement, Computer aided software engineering

L. Chen, Y. Li, J. Xu and C. S. Jensen, "Direction-Aware Why-Not Spatial Keyword Top-k Queries," 2017 IEEE 33rd International Conference on Data Engineering(ICDE), San Diego, California, USA, 2017, pp. 107-110.
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