Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDE.2007.367861
Title: Efficiently processing continuous k-NN queries on data streams
Authors: Böhm, C.
Ooi, B.C. 
Plant, C.
Yan, Y.
Issue Date: 2007
Source: Böhm, C.,Ooi, B.C.,Plant, C.,Yan, Y. (2007). Efficiently processing continuous k-NN queries on data streams. Proceedings - International Conference on Data Engineering : 156-165. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2007.367861
Abstract: Efficiently processing continuous k-nearest neighbor queries on data streams is important in many application domains, e. g. for network intrusion detection. Usually not all valid data objects from the stream can be kept in main memory. Therefore, most existing solutions are approximative. In this paper, we propose an efficient method for exact k-NN monitoring. Our method is based on three ideas, (1) selecting exactly those objects from the stream which are able to become the nearest neighbor of one or more continuous queries and storing them in a skyline data structure, (2) delaying to process those objects which are not immediately nearest neighbors of any query, and (3) indexing the queries rather than the streaming objects. In an extensive experimental evaluation we demonstrate that our method is applicable on high throughput data streams requiring only very limited storage. © 2007 IEEE.
Source Title: Proceedings - International Conference on Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/42010
ISBN: 1424408032
ISSN: 10844627
DOI: 10.1109/ICDE.2007.367861
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