Direction-Aware why-not spatial keyword Top-k queries

Lei Chen, Yafei Li, Jianliang XU, Christian S. Jensen

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781509065431
Publication statusPublished - 16 May 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: 19 Apr 201722 Apr 2017

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/TerritoryUnited States
CitySan Diego

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems


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