On skyline groups

Chengkai Li, Nan Zhang, Naeemul Hassan, Sundaresan Rajasekaran, Gautam Das

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

26 Scopus citations

Abstract

We formulate and investigate the novel problem of finding the skyline k-tuple groups from an n-tuple dataset - i.e., groups of k tuples which are not dominated by any other group of equal size, based on aggregate-based group dominance relationship. The major technical challenge is to identify effective anti-monotonic properties for pruning the search space of skyline groups. To this end, we show that the anti-monotonic property in the well-known Apriori algorithm does not hold for skyline group pruning. We then identify order-specific property which applies to SUM, MIN, and MAX and weak candidate-generation property which applies to MIN and MAX only. Experimental results on both real and synthetic datasets verify that the proposed algorithms achieve orders of magnitude performance gain over a baseline method.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages2119-2123
Number of pages5
DOIs
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

All Science Journal Classification (ASJC) codes

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

Cite this

Li, C., Zhang, N., Hassan, N., Rajasekaran, S., & Das, G. (2012). On skyline groups. In CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 2119-2123). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2396761.2398585