A Threshold-Based Algorithm for Continuous Monitoring of K Nearest Neighbors
Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naïve solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm for the continuous monitoring of nearest neighbors that minimizes the communication overhead between the server and the data objects. The proposed method can be used with multiple, static, or moving queries, for any distance definition, and does not require additional knowledge (e.g., velocity vectors) besides object locations.
Kyriakos MOURATIDIS, Dimitris Papadias, Spiridon Bakiras, and Yufei Tao. "A Threshold-Based Algorithm for Continuous Monitoring of K Nearest Neighbors" IEEE Transactions on Knowledge and Data Engineering 17.11 (2005): 1451-1464.
Available at: http://works.bepress.com/kyriakos_mouratidis/2