Distributed Approach to Continuous Queries with kNN Join Processing in Spatial Telemetric Data Warehouse

This chapter describes realization of distributed approach to continuous queries with kNN join processing in the spatial telemetric data warehouse. Due to dispersion of the developed system, new structural members were distinguished: the mobile object simulator, the kNN join processing service, and the query manager. Distributed tasks communicate using JAVA RMI methods. The kNN queries (k Nearest Neighbour) joins every point from one dataset with its k nearest neighbours in the other dataset. In our approach we use the Gorder method, which is a block nested loop join algorithm that exploits sorting, join scheduling, and distance computation filtering to reduce CPU and I/O usage.

A Data Stream Outlier Detection Algorithm Based on Reverse K Nearest Neighbors

This paper proposes a new data stream outlier detection algorithm SODRNN based on reverse nearest neighbors. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. The update of insertion or deletion only needs one scan of the current window, which improves efficiency. The capability of queries at arbitrary time on the whole current window is achieved by Query Manager Procedure, which can capture the phenomenon of concept drift of data stream in time. Results of experiments conducted on both synthetic and real data sets show that SODRNN algorithm is both effective and efficient.