On skyline groups

  • Authors:

  • Chengkai Li;Nan Zhang;Naeemul Hassan;Sundaresan Rajasekaran;Gautam Das

  • Affiliations:

  • University Texas at Arlington, Arlington, TX, USA;George Washington University, Washington, DC, USA;University Texas at Arlington, Arlington, TX, USA;George Washington University, Washington, DC, USA;University Texas at Arlington & Qatar Computing Research Institute, Arlington, TX, USA

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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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.