Sampling-Based Approximate Skyline Calculation on Big Data

Xiao, Xingxing; Li, Jianzhong

doi:10.1007/978-3-030-64843-5_3

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Notice that if U is the full space L, Sky U (D) is equivalent to the skyline [27] of D, abbreviated as Sky(D). In addition, for any U ⊆ L, Sky U (D) is the subset of Sky(D).…”

Section: Candidate Tuplesmentioning

confidence: 99%

Rank-Regret Minimization

Xiao¹,

Li²

2021

Preprint

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Multi-criteria decision-making often requires finding a small representative subset from the database. A recently proposed method is the regret minimization set (RMS) query. RMS returns a fixed size subset S of dataset D that minimizes the regret ratio of S (the difference between the score of top-1 in S and the score of top-1 in D, for any possible utility function). Existing work showed that the regret-ratio is not able to accurately quantify the "regret" level of a user. Further, relative to the regret-ratio, users do understand the notion of rank. Consequently, it considered the problem of finding a minimal set S with at most k rank-regret (the minimal rank of tuples of S in the sorted list of D).Corresponding to RMS, we focus on the dual version of the above problem, defined as the rank-regret minimization (RRM) problem, which seeks to find a fixed size set S that minimizes the maximum rank-regret for all possible utility functions. Further, we generalize RRM and propose the restricted rank-regret minimization (RRRM) problem to minimize the rank-regret of S for functions in a restricted space. The solution for RRRM usually has a lower regret level and can better serve the specific preferences of some users. In 2D space, we design a dynamic programming algorithm 2DRRM to find the optimal solution for RRM. In HD space, we propose an algorithm HDRRM for RRM that bounds the output size and introduces a double approximation guarantee for rank-regret. Both 2DRRM and HDRRM can be generalized to the RRRM problem. Extensive experiments are performed on the synthetic and real datasets to verify the efficiency and effectiveness of our algorithms.

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“…Notice that if U is the full space L, Sky U (D) is equivalent to the skyline [27] of D, abbreviated as Sky(D). In addition, for any U ⊆ L, Sky U (D) is the subset of Sky(D).…”

Section: Candidate Tuplesmentioning

confidence: 99%