distributed spatio-temporal similarity search
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, wild life monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions.
spatio-temporal similarity search top-k query processing