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.
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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.
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Han, S., Zheng, J., Dong, Q. (2018). Efficient Processing of k-regret Queries via Skyline Priority. 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_38
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DOI: https://doi.org/10.1007/978-3-030-02934-0_38
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