Common influence join: A natural join operation for spatial pointsets

Abstract

We identify and formalize a novel join operator for two spatial pointsets P and Q. The common influence join (CIJ) returns the pairs of points (p, q),p ∈ P, q ∈ Q, such that there exists a location in space, being closer to p than to any other point in P and at the same time closer to q than to any other point in Q. In contrast to existing join operators between pointsets (i.e., e-distance joins and k-closest pairs), CIJ is parameter-free, providing a natural join result that finds application in marketing and decision support. We propose algorithms for the efficient evaluation of CIJ, for pointsets indexed by hierarchical multi-dimensional indexes. We validate the effectiveness and the efficiency of these methods via experimentation with synthetic and real spatial datasets. The experimental results show that a non-blocking algorithm, which computes intersecting pairs of Voronoi cells on-demand, is very efficient in practice, incurring only slightly higher I/O cost than the theoretical lower bound cost for the problem. © 2008 IEEE.