Please use this identifier to cite or link to this item: https://doi.org/10.1145/1376616.1376622
Title: ST2B-tree: A self-tunable spatio-temporal B+-tree index for moving objects
Authors: Chen, S. 
Ooi, B.C. 
Tan, K.-L. 
Nascimento, M.A.
Keywords: Data distribution
Index tuning
Location-based services
Moving object indexing
Self-tuning
Issue Date: 2008
Source: Chen, 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. https://doi.org/10.1145/1376616.1376622
Abstract: In 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.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/40424
ISBN: 9781605581026
ISSN: 07308078
DOI: 10.1145/1376616.1376622
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