Advertisement

Efficient Processing of k-regret Queries via Skyline Priority

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

Abstract

Extracting interesting points from a large database is an important problem in multi-criteria decision making. The recent proposed k-regret query attracted people’s attention because it does not require any complicated information from users and the output size is controlled within k for users easily to choose. However, most existing algorithms for k-regret query suffer from a heavy burden by taking the numerous skyline points as candidate set. In this paper, we define a subset of candidate points from skyline points, called prior skyline points, so that the k-regret algorithms can be applied efficiently on the smaller candidate set to improve their performance. A useful metric called skyline priority is proposed to help determine the candidate set and corresponding strategies are applied to accelerate the algorithm. Experiments on synthetic and real datasets show the efficiency and effectiveness of our proposed method.

Keywords

k-regret query Skyline priority Prior skyline points Candidate set determination 

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 KFJJ20171601.

References

  1. 1.
    Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of Top-k query processing techniques in relational database systems. ACM Comput. Surv. 40(4), 1–58 (2008)CrossRefGoogle Scholar
  2. 2.
    Börzsöny, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)Google Scholar
  3. 3.
    Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. VLDB 3, 1114–1124 (2010)Google Scholar
  4. 4.
    Agarwal, P.K., Kumar, N., Sintos, S., Suri, S.: Efficient algorithms for k-regret minimizing sets. CoRR, abs/1702.01446 (2017)Google Scholar
  5. 5.
    Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: SIGMOD (2018)Google Scholar
  6. 6.
    Godfrey, P.: Skyline cardinality for relational processing. In: Seipel, D., Turull-Torres, J.M. (eds.) FoIKS 2004. LNCS, vol. 2942, pp. 78–97. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24627-5_7CrossRefGoogle Scholar
  7. 7.
    Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: the k most representative skyline operator. In: ICDE, pp. 86–95 (2007)Google Scholar
  8. 8.
    Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: ICDE, pp. 892–903 (2009)Google Scholar
  9. 9.
    Qi, J., Zuo, F., Samet, H., Yao, J.C.: From additive to multiplicative utilities, K-regret queries (2016)Google Scholar
  10. 10.
    Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J.X., Zhang, Q.: Efficient computation of the skyline cube. In: VLDB, pp. 241–252 (2005)Google Scholar
  11. 11.
    Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: a semantic approach based on decisive subspaces. In: VLDB, pp. 253–264 (2005)Google Scholar
  12. 12.
    Nanongkai, D., Lall, A., Das Sarma, A., Makino, K.: Interactive regret minimization. In: SIGMOD, pp. 109–120 (2012)Google Scholar
  13. 13.
    Chester, S., Thomo, A., Venkatesh, S., Whitesides, S.: Computing k-regret minimizing sets. In: VLDB, pp. 389–400 (2014)CrossRefGoogle Scholar

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

Personalised recommendations