Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2005
Abstract
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.
Keywords
Location-dependent and sensitive, Query processing, Spatial databases, Velocity vectors
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
17
Issue
11
First Page
1451
Last Page
1464
ISSN
1041-4347
Identifier
10.1109/TKDE.2005.172
Publisher
IEEE
Citation
MOURATIDIS, Kyriakos; Papadias, Dimitris; Bakiras, Spiridon; and TAO, Yufei.
A Threshold-Based Algorithm for Continuous Monitoring of K Nearest Neighbors. (2005). IEEE Transactions on Knowledge and Data Engineering. 17, (11), 1451-1464.
Available at: https://ink.library.smu.edu.sg/sis_research/125
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1109/TKDE.2005.172
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons