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An Efficient Algorithm for Computing k-Average-Regret Minimizing Sets in Databases

  • Xianhong Qiu
  • Jiping ZhengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)

Abstract

Returning a small set of data points instead of the whole dataset to a user is a major task of a database system which has been studied extensively in recent years. In this paper, we study k-average-regret query, a recently proposed query, which uses “average regret ratio" as a metric to measure users’ satisfaction to avoid the biases towards a few dissatisfied users that the best-known k-regret query suffers from. The main challenge of executing a k-average-regret query is the low efficiency of existing algorithms. Fortunately, as the average regret function exhibits the properties of supermodularity and monotonictity, the computational complexity of k-average-regret query can be significantly reduced exploiting lazy evaluations, thus leading to our accelerated algorithm which we called Lazy-Greedy. Experiments on both synthetic and real datasets confirm the efficiency and quality of output of our proposed algorithm.

Keywords

k-average-regret query Representative skyline Lazy evaluation 

Notes

Acknowledgment

This work is partially supported by the National Natural Science Foundation of China under grants U1733112,61702260, Funding of Graduate Innovation Center in NUAA under grant KFJJ20171605.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

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