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On Skyline Groups
April 2014 (vol. 26 no. 4)
pp. 942-956
Naeemul Hassan, Dept. of Comput. Sci. & Eng., Univ. of Texas at Arlington, Arlington, TX, USA
Sundaresan Rajasekaran, Dept. of Comput. Sci., George Washington Univ., Washington, DC, USA
Gautam Das, Dept. of Comput. Sci. & Eng., Univ. of Texas at Arlington, Arlington, TX, USA
Nan Zhang, Dept. of Comput. Sci., George Washington Univ., Washington, DC, USA
Chengkai Li, Dept. of Comput. Sci. & Eng., Univ. of Texas at Arlington, Arlington, TX, USA
We formulate and investigate the novel problem of finding the skyline k-tuple groups from an n-tuple data set-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 first show that the anti-monotonic property in the well-known Apriori algorithm does not hold for skyline group pruning. Then, we identify two anti-monotonic properties with varying degrees of applicability: order-specific property which applies to SUM, MIN, and MAX as well as weak candidate-generation property which applies to MIN and MAX only. Experimental results on both real and synthetic data sets verify that the proposed algorithms achieve orders of magnitude performance gain over the baseline method.
Index Terms:
Aggregates,Vectors,Games,Databases,Computer science,Educational institutions,Electronic mail,anti-monotonic properties,Skyline queries,skyline groups
Citation:
Naeemul Hassan, Sundaresan Rajasekaran, Gautam Das, Nan Zhang, Chengkai Li, "On Skyline Groups," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 4, pp. 942-956, April 2014, doi:10.1109/TKDE.2013.119
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