CLUR-Tree for Supporting Frequent Updates of Data Stream over Sensor Networks

Abstract

Data streams from sensors are usually characterized as continuous, with very frequent updates. Queries over those data streams need to be processed in near real-time. So it is needed to design the index structure for supporting the frequent updates and fast retrieval of data efficiently. In this paper, CLUR-Tree (Cache-conscious Lazy Update R-Tree) is proposed, which is a spatial index for efficient processing of frequent updates of data streams in locality preserving monitoring applications. CLUR-Tree has two characteristics. First, it excludes index reconstruction overhead by permitting modification of only the index node of the sensor which moves out of the corresponding MBR (Minimum Bound Rectangle). Second, it reduces the key spaces by applying new compression method for MBR used as key in R-Tree and by considering cache to prevent bottleneck due to speed difference between main memory and CPU. The experimental results indicate that the proposed CLUR-Tree enhances update performance and gives a good retrieval performance simultaneously.

This research was supported by the MIC (Ministry of Information and Communication), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Assessment).