Integrating non-spatial preferences into spatial location queries

Qiang Qu, Siyuan Liu, Bin Yang, Christian S. Jensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


Increasing volumes of geo-referenced data are becoming available. This data includes so-called points of interest that describe businesses, tourist attractions, etc. by means of a geo-location and properties such as a textual description or ratings. We propose and study the efficient implementation of a new kind of query on points of interest that takes into account both the locations and properties of the points of interest. The query takes a result cardinality, a spatial range, and property-related preferences as parameters, and it returns a compact set of points of interest with the given cardinality and in the given range that satisfies the preferences. Specifically, the points of interest in the result set cover so-called allying preferences and are located far from points of interest that possess so-called alienating preferences. A unified result rating function integrates the two kinds of preferences with spatial distance to achieve this functionality. We provide efficient exact algorithms for this kind of query. To enable queries on large datasets, we also provide an approximate algorithm that utilizes a nearest-neighbor property to achieve scalable performance. We develop and apply lower and upper bounds that enable search-space pruning and thus improve performance. Finally, we provide a generalization of the above query and also extend the algorithms to support the generalization. We report on an experimental evaluation of the proposed algorithms using real point of interest data from Google Places for Business that offers insight into the performance of the proposed solutions.

Original languageEnglish (US)
Title of host publicationSSDBM 2014 - Proceedings of the 26th International Conference on Scientific and Statistical Database Management
PublisherAssociation for Computing Machinery
ISBN (Print)9781450327220
StatePublished - 2014
Event26th International Conference on Scientific and Statistical Database Management, SSDBM 2014 - Aalborg, Denmark
Duration: Jun 30 2014Jul 2 2014

Publication series

NameACM International Conference Proceeding Series


Other26th International Conference on Scientific and Statistical Database Management, SSDBM 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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