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Efficient Approximate Algorithms for k-Regret Queries with Binary Constraints

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

Abstract

Extracting interesting points from a large dataset is an important problem for multi-criteria decision making. Recently, k-regret query was proposed and received attentions from the database community because it does not require any utility function from users and the output size is controllable. In this paper, we consider k-regret queries with binary constraints which doesn’t be addressed before. Given a collection of binary constraints, we study the problem of extracting the k representative points with small regret ratio while satisfying the binary constraints. To express the satisfaction of data points by the binary constraints, we propose two models named NBC and LBC in quantitative and qualitative ways respectively. In quantitative way, the satisfaction is expressed by a real number between 0 and 1 which quaNtifies the satisfaction of a point by the Binary Constraints. While in qualitative way, the satisfaction is modeled quaLitatively by a set of Binary Constraints which are satisfied by the point. Further, two efficient approximate algorithms called \(NBC_P\)-Greedy and \(NBC_{DN}\)-Greedy are developed based on NBC while \(LBC_{DN}\)-Greedy algorithm is proposed based on LBC. Extensive experiments on synthetic and real datasets confirm the efficiency and effectiveness of our proposed algorithms.

Keywords

k-Regret query Binary constraints Multi-criteria decision making Top-k query Skyline query 

Notes

Acknowledgment

This work is partially supported by the National Natural Science Foundation of China under grants U1733112,61702260, the Natural Science Foundation of Jiangsu Province of China under grant BK20140826, the Fundamental Research Funds for the Central Universities under grant NS2015095, Funding of Graduate Innovation Center in NUAA under grant KFJJ20171605.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qi Dong
    • 1
  • Jiping Zheng
    • 1
    • 2
    Email author
  • Xianhong Qiu
    • 1
  • Xingnan Huang
    • 1
  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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