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.
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Agarwal, P.K., Kumar, N., Sintos, S., Suri, S.: Efficient algorithms for k-regret minimizing sets. In: International Symposium on Experimental Algorithms, pp. 7:1–7:23 (2017)
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)
Borzsony, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)
Cao, W., et al.: k-regret minimizing set: efficient algorithms and hardness. In: ICDT, pp. 11:1–11:19 (2017)
Chan, C.-Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: Finding k-dominant skylines in high dimensional space. In: SIGMOD, pp. 503–514 (2006)
Chester, S., Thomo, A., Venkatesh, S., Whitesides, S.: Computing k-regret minimizing sets. In: VLDB, pp. 389–400 (2014)
Faulkner, T.K., Brackenbury, W., Lall, A.: k-regret queries with nonlinear utilities. In: VLDB, pp. 2098–2109 (2015)
Guha, S., Gunopoulos, D., Vlachos, M., Koudas, N., Srivastava, D.: Efficient approximation of optimization queries under parametric aggregation constraints. In: VLDB, pp. 778–789 (2003)
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), 11:1–11:58 (2008)
Khan, A., Singh, V.: Top-k representative queries with binary constraints. In: SSDBM, pp. 13:1–13:10 (2015)
Lee, J., won You, G., won Hwang, S.: Personalized top-k skyline queries in high-dimensional space. Inf. Syst. 34(1), 45–61 (2009)
Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: the k most representative skyline operator. In: ICDE, pp. 86–95 (2007)
Mindolin, D., Chomicki, J.: Discovering relative importance of skyline attributes. In: VLDB, pp. 610–621 (2009)
Nanongkai, D., Lall, A., Das Sarma, A., Makino, K.: Interactive regret minimization. In: SIGMOD, pp. 109–120 (2012)
Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. VLDB 3(1–2), 1114–1124 (2010)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. TODS 30(1), 41–82 (2005)
Papadimitriou, C.H., Yannakakis, M.: On the approximability of trade-offs and optimal access of web sources. In: FOCS, pp. 86–92 (2000)
Peng, P., Wong, R.C.W.: Geometry approach for k-regret query. In: ICDE, pp. 772–783 (2014)
Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: ICDE, pp. 892–903 (2009)
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: Proceedings of the 2018 International Conference on Management of Data, pp. 959–974 (2018)
Zhang, Z., Hwang, S.W., Chang, C.C., Wang, M., Lang, C.A., Chang, Y.C.: Boolean + ranking: querying a database by k-constrained optimization. In: SIGMOD, pp. 359–370 (2006)
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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Dong, Q., Zheng, J., Qiu, X., Huang, X. (2018). Efficient Approximate Algorithms for k-Regret Queries with Binary Constraints. 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_39
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DOI: https://doi.org/10.1007/978-3-030-02934-0_39
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