Efficiently Processing Continuous k-NN Queries on Data Streams

Efficiently processing continuous k-nearest neighbor queries on data streams is important in many application domains, e. g. for network intrusion detection or in querysubscriber systems. Usually not all valid data objects from the stream can be kept in main memory. Therefore, most existing solutions immediately discard some of the objects and store only representative objects in an index. These solutions are thus 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) indexing the queries rather than the streaming objects, and (3) delaying to process those objects which are not immediately nearest neighbors of any query. In an extensive experimental evaluation we demonstrate that our method is applicable on high throughput data streams re...

• Type: Conference
• Year: 2007
• Where: ICDE
• Authors: Beng Chin Ooi, Christian Böhm, Claudia Plant, Ying Yan
• Keywords: Database, ICDE 2007, K-nearest Neighbor Queries, Representative Objects, Valid Data Objects