PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing moving points (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Change Tolerant Indexing for Constantly Evolving Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Traditional spatial indexes like R-tree usually assume the database is not updated frequently. In applications like location-based services and sensor networks, this assumption is no longer true since data updates can be numerous and frequent. As a result these indexes can suffer from a high update overhead, leading to poor performance. In this paper we propose a novel index structure, the Mean Variance Tree (MVTree), which is built based on the mean and variance of the data instead of the actual data values that can change continuously. Since the mean and variance are relatively stable features compared to the actual values, the MVTree significantly reduces the index update cost. The mean and the variance of the data item can be dynamically adjusted to match the observed fluctuation of the data. Our experiments show that the MVTree substantially improves index update performance while maintaining satisfactory query performance.