The most-likely skyline problem for stochastic points

Akash Agrawal, Yuan Li, Jie Xue, Ravi Janardan

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

For a set O of n points in Rd, the skyline consists of the subset of all points of O where no point is dominated by any other point of O. Suppose that each point oi ∈ O has an associated probability of existence pi ∈ (0, 1]. The problem of computing the skyline with the maximum probability of occurrence is considered. It is shown that in Rd, d ≥ 3, the problem is NP-hard and that the desired skyline cannot even be well-approximated in polynomial-time unless P = NP. In R2, an optimal O(n log n)-time and O(n)-space algorithm is given.

Original languageEnglish (US)
Pages78-83
Number of pages6
StatePublished - Jan 1 2017
Event29th Canadian Conference on Computational Geometry, CCCG 2017 - Ottawa, Canada
Duration: Jul 26 2017Jul 28 2017

Conference

Conference29th Canadian Conference on Computational Geometry, CCCG 2017
CountryCanada
CityOttawa
Period7/26/177/28/17

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