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, L., Li, Y., Xu, J., & Jensen, C. (2018). Towards why-not spatial keyword top-k queries: A direction-aware approach. IEEE Transactions on Knowledge and Data Engineering, 30 (4), 796-809. https://doi.org/10.1109/TKDE.2017.2778731