Closest-Point-of-Approach Join for Moving Object Histories

In applications that produce a large amount of data describing the paths of moving objects, there is a need to ask questions about the interaction of objects over a long recorded history. In this paper, we consider the problem of computing joins over massive moving object histories. The particular join that we study is the "Closest-Point-Of- Approach" join, which asks: Given a massive moving object history, which objects approached within a distance ‘d’ of one another? We carefully consider several relatively obvious strategies for computing the answer to such a join, and then propose a novel, adaptive join algorithm which naturally alters the way in which it computes the join in response to the characteristics of the underlying data.