Abstract
Helping end-users to find the most desired points in the database is an important task for database systems to support multi-criteria decision making. The recent proposed k-regret query doesn’t ask for elaborate information and can output k points 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 aim at decreasing the candidate points from skyline points to a relative small subset of skyline points, called frequent skyline points, so that the k-regret algorithms can be applied efficiently on the smaller candidate set to improve their efficiency. A useful metric based on subspace skyline called skyline frequency is adopted to help determine the candidate set and corresponding algorithm is developed. Experiments on synthetic and real datasets show the efficiency and effectiveness of our proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Börzsöny, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)
Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. VLDB 3, 1114–1124 (2010)
Chester, S., Thomo, A., Venkatesh, S., Whitesides, S.: Computing k-regret minimizing sets. VLDB 7, 389–400 (2014)
Asudeh, A., Nazi, A., Zhang, N., Das, G.: Efficient computation of regret-ratio minimizing set: a compact maxima representative. In: SIGMOD, pp. 821–834 (2017)
Agarwal, P.K., Kumar, N., Sintos, S., Suri, S.: Efficient algorithms for k-regret minimizing sets. CoRR, abs/1702.01446 (2017)
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)
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_7
Qi, J., Zuo, F., Samet, H., Yao, J.C.: K-regret queries: from additive to multiplicative utilities (2016)
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)
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)
Chan, C.-Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: On high dimensional skylines. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 478–495. Springer, Heidelberg (2006). https://doi.org/10.1007/11687238_30
Karp, R.M., Luby, M., Madras, N.: Monte Carlo approximation algorithms for enumeration problems. J. Algorithms 10(3), 429–448 (1989)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, S., Zheng, J., Dong, Q. (2018). Efficient Processing of k-regret Queries via Skyline Frequency. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-02934-0_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02933-3
Online ISBN: 978-3-030-02934-0
eBook Packages: Computer ScienceComputer Science (R0)