Efficient methods for finding influential locations with adaptive grids.

Given a set S of servers and a set C of clients, an optimal-location query returns a location where a new server can attract the greatest number of clients. Optimal-location queries are important in a lot of real-life applications, such as mobile service planning or resource distribution in an area. Previous studies assume that a client always visits its nearest server, which is too strict to be true in reality. In this paper, we relax this assumption and propose a new model to tackle this problem. We further generalize the problem to finding top-k optimal locations. The main challenge is that, even the fastest approach in existing studies needs to take hours to answer an optimal-location query on a typical real world dataset, which significantly limits the applications of the query. Using our relaxed model, we design an efficient grid-based approximation algorithm called FILM (Fast Influential Location Miner) to the queries, which is orders of magnitude faster than the best-known previous work and the number of clients attracted by a new server in the result location often exceeds 98% of the optimal. The algorithm is extended to finding k influential locations. Extensive experiments are conducted to show the efficiency and effectiveness of FILM on both real and synthetic datasets.