Making B+- trees cache conscious in main memory
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimizing multidimensional index trees for main memory access
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Compressing Relations and Indexes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Peer-to-Peer Spatial Queries in Sensor Networks
P2P '03 Proceedings of the 3rd International Conference on Peer-to-Peer Computing
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Supporting frequent updates in R-trees: a bottom-up approach
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Organization and maintenance of large ordered indices
SIGFIDET '70 Proceedings of the 1970 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control
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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.