Department of Computer Science
With the continued proliferation of location-based services, a growing number of web-accessible data objects are geo-tagged 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 direction-aware queries. We prove that the best refined query directions lie in a finite solution space for a special case and reduce the search for the optimal refinement to a linear programming problem for the general case. Extensive experimental studies demonstrate that the proposed techniques outperform a baseline method by two orders of magnitude and are robust in a broad range of settings.
Why-not questions, spatial keyword top-k queries, query refinement, Indexes, Search problems, Spatial databases, Legged locomotion, Query processing, Linear programming
Source Publication Title
IEEE Transactions on Knowledge and Data Engineering
Institute of Electrical and Electronics Engineers
© Copyright 2018 IEEE - All rights reserved.
This work is supported by HK-RGC Grants 12201615, 12244916, and 12200817. The work of Yafei Li is supported by NSFC Grant 61602420.
Link to Publisher's Edition
Chen, Lei, Yafei Li, Jianliang Xu, and Christian S. Jensen. "Towards why-not spatial keyword top-k queries: A direction-aware approach." IEEE Transactions on Knowledge and Data Engineering 30.4 (2018): 796-809.
Available for download on Tuesday, May 01, 2018