Flexible Aggregate Nearest Neighbor Queries in Road Networks

Aggregate nearest neighbor (ANN) query has been studied in both the Euclidean space and road networks. The flexible aggregate nearest neighbor ( FANN) problem further generalizes ANN by introducing an extra flexibility. Given a set of data points P, a set of query points Q, and a user-defined flexibility parameter phi that ranges in (0, 1], an FANN query returns the best candidate from P, which minimizes the aggregate (usually max or sum) distance to any phi vertical bar Q vertical bar objects in Q. In this paper, we focus on the problem in road networks (denoted as FANN(R)), and present a series of universal (i.e., suitable for both max and sum) algorithms to answer FANN(R) queries in road networks, including a Dijkstra-based algorithm enumerating P, a queue-based approach that processes data points from-near-to-far, and a framework that combines Incremental Euclidean Restriction (IER) and kNN. We also propose a specific exact solution to max-FANN(R) and a specific approximate solution to sum-FANN(R) which can return a near-optimal result with a guaranteed constant-factor approximation. These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend the FANN(R) to k-FANN(R), and successfully adapt most of the proposed algorithms to answer k-FANN(R) queries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real road networks to demonstrate their superior efficiency and high quality.