Representative skylines using threshold-based preference distributions

Authors:
Atish Das Sarma;Ashwin Lall;Danupon Nanongkai;Richard J. Lipton;Jim Xu
Affiliations:
College of Computing, Georgia Institute of Technology, 266 Ferst Dr., Atlanta, 30332, USA;College of Computing, Georgia Institute of Technology, 266 Ferst Dr., Atlanta, 30332, USA;College of Computing, Georgia Institute of Technology, 266 Ferst Dr., Atlanta, 30332, USA;College of Computing, Georgia Institute of Technology, 266 Ferst Dr., Atlanta, 30332, USA;College of Computing, Georgia Institute of Technology, 266 Ferst Dr., Atlanta, 30332, USA
Venue:
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Year:
2011

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Abstract

The study of skylines and their variants has received considerable attention in recent years. Skylines are essentially sets of most interesting (undominated) tuples in a database. However, since the skyline is often very large, much research effort has been devoted to identifying a smaller subset of (say k) "representative skyline" points. Several different definitions of representative skylines have been considered. Most of these formulations are intuitive in that they try to achieve some kind of clustering "spread" over the entire skyline, with k points. In this work, we take a more principled approach in defining the representative skyline objective. One of our main contributions is to formulate the problem of displaying k representative skyline points such that the probability that a random user would click on one of them is maximized.