Answering why-not questions on spatial keyword top-k queries

Lei Chen, Xin Lin, Haibo Hu, Christian S. Jensen, Jianliang Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

57 Citations (Scopus)


Large volumes of geo-tagged text objects are available on the web. Spatial keyword top-k queries retrieve k such objects with the best score according to a ranking function that takes into account a query location and query keywords. In this setting, users may wonder why some known object is unexpectedly missing from a result; and understanding why may aid users in retrieving better results. While spatial keyword querying has been studied intensively, no proposals exist for how to offer users explanations of why such expected objects are missing from results. We provide techniques that allow the revision of spatial keyword queries such that their results include one or more desired, but missing objects. In doing so, we adopt a query refinement approach to provide a basic algorithm that reduces the problem to a two-dimensional geometrical problem. To improve performance, we propose an index-based ranking estimation algorithm that prunes candidate results early. Extensive experimental results offer insight into design properties of the proposed techniques and suggest that they are efficient in terms of both running time and I/O cost.
Original languageEnglish
Title of host publication2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781479979639
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event2015 31st IEEE International Conference on Data Engineering, ICDE 2015 - Seoul, Korea, Republic of
Duration: 13 Apr 201517 Apr 2015


Conference2015 31st IEEE International Conference on Data Engineering, ICDE 2015
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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