A Threshold-Based Algorithm for Continuous Monitoring of K Nearest Neighbors

• Main Authors: MOURATIDIS, Kyriakos, Papadias, Dimitris, Bakiras, Spiridon, TAO, Yufei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2005
• Subjects: Location-dependent and sensitive; Query processing; Spatial databases; Velocity vectors; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/125; https://ink.library.smu.edu.sg/context/sis_research/article/1124/viewcontent/TKDE05_CNN.pdf
• Institution: Singapore Management University

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.