Authors: D. Zeinalipour-Yazti, S. Lin, D. Gunopulos,

Title: Distributed Spatio-Temporal Similarity Search

Conference: ACM 15th Conference on Information and Knowledge Management, (CIKM 2006)

Year: 2006

Abstract: In this paper we introduce the distributed spatio-temporal similarity search problem: given a query trajectory Q, we want to find the trajectories that follow a motion similar to Q, when each of the target trajectories is segmented across a number of distributed nodes. We propose two novel algorithms, UB-K and UBLB-K, which combine local computations of lower and upper bounds on the matching between the distributed subsequences and Q. Such an operation generates the desired result without pulling together all the distributed subsequences over the fundamentally expensive communication medium. Our solutions find applications in a wide array of domains, such as cellular networks, wildlife monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions.

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