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dc.titleST2B-tree: A self-tunable spatio-temporal B+-tree index for moving objects
dc.contributor.authorChen, S.
dc.contributor.authorOoi, B.C.
dc.contributor.authorTan, K.-L.
dc.contributor.authorNascimento, M.A.
dc.identifier.citationChen, S.,Ooi, B.C.,Tan, K.-L.,Nascimento, M.A. (2008). ST2B-tree: A self-tunable spatio-temporal B+-tree index for moving objects. Proceedings of the ACM SIGMOD International Conference on Management of Data : 29-42. ScholarBank@NUS Repository. <a href="" target="_blank"></a>
dc.description.abstractIn a moving objects database (MOD) the dataset and the workload change frequently. As the locations of objects change in space and time, the data distribution also changes and the answer for a same query over the same region may vary widely over time. As a result, traditional static indexes are not able to perform well and it is critical to develop self-tuning indexes that can be reconfigured automatically based on the state of the system. Towards this goal we propose the ST2B-tree, a Self- Tunable Spatio- Temporal B +-Tree index for MODs, which is amenable to tuning. Frequent updates to its subtrees allows rebuilding (tuning) a subtree using a different set of reference points and different grid size without significant overhead. We also present an online tuning framework for the ST2B-tree, where the tuning is conducted online and automatically without human intervention, also not interfering with regular functions of the MOD. Our extensive experiments show that the self-tuning process minimizes the effectiveness degradation of the index caused by workload changes at the cost of virtually no overhead. Copyright 2008 ACM.
dc.subjectData distribution
dc.subjectIndex tuning
dc.subjectLocation-based services
dc.subjectMoving object indexing
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings of the ACM SIGMOD International Conference on Management of Data
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