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 φ 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 φ |Q| 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.