The farthest spatial skyline queries

Gae Won You, Mu Woong Lee, Hyeonseung Im, Seung Won Hwang

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Pareto-optimal objects are favored as each of such objects has at least one competitive edge against all other objects, or "not dominated". Recently, in the database literature, skyline queries have gained attention as an effective way to identify such pareto-optimal objects. In particular, this paper studies the pareto-optimal objects in perspective of facility or business locations. More specifically, given data points P and query points Q in two-dimensional space, our goal is to retrieve data points that are farther from at least one query point than all the other data points. Such queries are helpful in identifying spatial locations far away from undesirable locations, e.g., unpleasant facilities or business competitors. To solve this problem, we first study a baseline Algorithm TFSS and propose an efficient progressive Algorithm BBFS, which significantly outperforms TFSS by exploiting spatial locality. We also develop an efficient approximation algorithm to trade accuracy for efficiency. We validate our proposed algorithms using extensive evaluations over synthetic and real datasets.

Original languageEnglish
Pages (from-to)286-301
Number of pages16
JournalInformation Systems
Issue number3
Publication statusPublished - 2013

Bibliographical note

Funding Information:
This research was supported by the National IT Industry Promotion Agency (NIPA) under the program of Software Engineering Technologies Development.

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture


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