Incremental evaluation of top-κ combinatorial metric skyline query

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Author(s)

  • Tao Jiang
  • Bin Zhang
  • Dan Lin
  • Yunjun Gao
  • Qing Li

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)89-105
Journal / PublicationKnowledge-Based Systems
Volume74
Publication statusPublished - 2015

Abstract

In this paper, we define a novel type of skyline query, namely top-κ combinatorial metric skyline (κCMS) query. The κCMS query aims to find k combinations of data points according to a monotonic preference function such that each combination has the query object in its metric skyline. The κCMS query will enable a new set of location-based applications that the traditional skyline queries cannot offer. To answer the κCMS query, we propose two efficient query algorithms, which leverage a suite of techniques including the sorting and threshold mechanisms, reusing technique, and heuristics pruning to incrementally and quickly generate combinations of possible query results. We have conducted extensive experimental studies, and the results demonstrate both effectiveness and efficiency of our proposed algorithms.

Research Area(s)

  • Algorithm, Combinational skyline, Metric skyline, Query processing, Spatial database

Citation Format(s)

Incremental evaluation of top-κ combinatorial metric skyline query. / Jiang, Tao; Zhang, Bin; Lin, Dan; Gao, Yunjun; Li, Qing.

In: Knowledge-Based Systems, Vol. 74, 2015, p. 89-105.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review