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NetClus: A Scalable Framework to Mine Top-K Locations for Placement of Trajectory-Aware Services

Optimal location (OL) queries traditionally leverage only static user information to identify the best locations for installing new facilities. However, many services such as fuel stations, ATMs, etc. mostly serve mobile users and, therefore, their placement must take into account the user mobility patterns or trajectories as well. Hence, to find the best k locations on a road network that serves maximum number of user-trajectories, in this paper, we propose the TOPS (Trajectory-Aware Optimal Placement of Services) query. TOPS is an NP-hard problem. Primarily due to high memory overhead beyond the capabilities of commodity machines, the best known greedy heuristic is impractical and not scalable for real urban-scale scenarios. To this end, we design an efficient indexing framework called NetClus based on network clustering and FM sketches. The solution provides theoretical quality guarantees and bounded computational times. Experiments on real urban-scale datasets establish the efficiency and effectiveness of NetClus.