Identifying Top-K Optimal Locations for Placement of Large-Scale Trajectory-Aware Services

Optimal location problems identify the best sites to set up new facilities for providing service to its users. Majority of the existing work in this space assumes that the users are static and the datasets are small. Such assumptions are too restrictive and unrealistic for reallife services such as setting up of fuel stations, upgradation of cellphone base-stations, etc. The placement of such services should, however, factor in the mobility patterns of its consumers, i.e., the user trajectories. For example, given a budget of k locations to set up fuel stations, the objective should be to cover the maximum number of user trajectories. In this doctoral work, we introduce top-k optimal location problems for large-scale trajectory-aware services. Since these problems are NP-hard, we design scalable techniques with bounded quality guarantees that work directly with user trajectories over city-scale road networks. Empirical evaluations show that the proposed heuristics are highly efficient, both in terms of space overhead and running time, as well as quite effective, with quality close to that of the optimal.