Textually Relevant Spatial Skylines

Jieming Shi, Dingming Wu, Nikos Mamoulis

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

Abstract

We study the modeling and evaluation of a spatio-textual skyline (STS) query, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on their relevance to a set of query keywords. STS is especially relevant to modern applications, where points of interest are typically augmented with textual descriptions. We investigate three models for integrating textual relevance into the spatial skyline. Among them, model STD, which combines spatial distance with textual relevance in a derived dimensional space, is found to be the most effective one. STD computes a skyline which not only satisfies the intent of STS, but also has a small and easy-to-interpret size. We propose an efficient algorithm for computing STD-based skylines, which operates on an IR-tree that indexes the data. The effectiveness of our STD model and the efficiency of the proposed algorithm are evaluated on real data sets.

Original languageEnglish
Article number7181692
Pages (from-to)224-237
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume28
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes

Keywords

  • Query processing
  • spatial databases
  • textual databases

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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