MC2LS: Towards Efficient Collective Location Selection in Competition
Meng Wang, Mengfei Zhao, Hui Li, Jiangtao Cui, Bo Yang, Tao Xue · IEEE Transactions on Knowledge and Data Engineering · 2024
Collective Location Selection (CLS) has received significant research attention in the spatial database community due to its wide range of applications. The CLS problem selects a group ofkpreferred locations among candidate sites to establish facilities, aimed at collectively attracting the maximum number of users. Existing studies commonly assume every user is located in a fixed position, without considering the competition between peer facilities. Unfortunately, in real markets, users are mobile and choose to patronize from a host of competitors, making traditional techniques unavailable. To this end, this paper presents the first effort on a CLS problem in competition scenarios, calledmc$^{2}$2ls, taking into account the mobility factor. Solvingmc$^{2}$2lsis a non-trivial task due to its NP-hardness. To overcome the challenge of pruning multi-point users with highly overlapped minimum boundary rectangles (MBRs), we exploit a position count threshold and design two square-based pruning rules. We introduce IQuad-tree, a user-MBR-free index, to benefit the hierarchical and batch-wise properties of the pruning rules. We propose an$(1-\frac{1}{e})$-approximate greedy solution tomc$^{2}$2lsand incorporate a candidate-pruning strategy to further accelerate the computation for handling skewed datasets. Extensive experiments are conducted on real datasets, demonstrating the superiority of our proposed pruning rules and solution compared to the state-of-the-art techniques.