Document Type

Journal Article

Department/Unit

Department of Computer Science

Language

English

Abstract

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.

Keywords

Why-not questions, spatial keyword top-k queries, query refinement, Indexes, Search problems, Spatial databases, Legged locomotion, Query processing, Linear programming

Publication Date

4-2018

Source Publication Title

IEEE Transactions on Knowledge and Data Engineering

Volume

30

Issue

4

Start Page

796

End Page

809

Publisher

Institute of Electrical and Electronics Engineers

Peer Reviewed

1

Copyright

© Copyright 2018 IEEE - All rights reserved.

Funder

This work is supported by HK-RGC Grants 12201615, 12244916, and 12200817. The work of Yafei Li is supported by NSFC Grant 61602420.

DOI

10.1109/TKDE.2017.2778731

Link to Publisher's Edition

http://dx.doi.org/10.1109/TKDE.2017.2778731

ISSN (print)

10414347

ISSN (electronic)

15582191

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