An Efficient Algorithm for Computing k-Average-Regret Minimizing Sets in Databases
Returning a small set of data points instead of the whole dataset to a user is a major task of a database system which has been studied extensively in recent years. In this paper, we study k-average-regret query, a recently proposed query, which uses “average regret ratio" as a metric to measure users’ satisfaction to avoid the biases towards a few dissatisfied users that the best-known k-regret query suffers from. The main challenge of executing a k-average-regret query is the low efficiency of existing algorithms. Fortunately, as the average regret function exhibits the properties of supermodularity and monotonictity, the computational complexity of k-average-regret query can be significantly reduced exploiting lazy evaluations, thus leading to our accelerated algorithm which we called Lazy-Greedy. Experiments on both synthetic and real datasets confirm the efficiency and quality of output of our proposed algorithm.
Keywordsk-average-regret query Representative skyline Lazy evaluation
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 KFJJ20171605.
- 2.Börzsöny, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)Google Scholar
- 4.Zeighami, S., Wong, R.C.W.: Minimizing average regret ratio in database. In: SIGMOD, pp. 2265–2266 (2016)Google Scholar
- 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.Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: ICDE, pp. 892–903 (2009)Google Scholar
- 9.Peng, P., Wong, R.C.W.: Geometry approach for k-regret query. In: ICDE, pp. 772–783 (2014)Google Scholar
- 10.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