(Approximate) Uncertain Skylines |
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Authors: | Peyman Afshani Pankaj K. Agarwal Lars Arge Kasper Green Larsen Jeff M. Phillips |
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Affiliation: | 1. Faculty of Computer Science, Dalhousie University, 6050 University Ave., Halifax, NS, Canada 2. Department of Computer Science, Duke University, Box 90129, Durham, NC, 27708-0129, USA 3. MADALGO & Department of Computer Science, University of Aarhus, IT-Parken, Aabogade 34, 8200, Aarhus N, Denmark 4. School of Computing, University of Utah, 50 S Central Campus Dr. 3190, Salt Lake City, UT, 84112, USA
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Abstract: | Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution. We also suggest why we believe our solution might be optimal. Next, we describe simple, near-linear time approximation algorithms for computing the probability of each point lying on the skyline. In addition, some of our methods can be adapted to construct data structures that can efficiently determine the probability of a query point lying on the skyline. |
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