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Return to DBMS in the sciences
With the rapid increase in the use of inexpensive, location-aware sensors in a variety of new applications, large amounts of time-sequenced location data will soon be accumulated. Efficient indexing techniques for managing these large volumes of trajectory data sets are urgently needed. The key requirements for a good trajectory indexing technique is that it must support both searches and inserts efficiently. This paper proposes a new indexing mechanism called SETI, a Scalable and Efficient Trajectory Index, that meets these requirements. SETI uses a simple two-level index structure to decouple the indexing of the spatial and the temporal dimensions. This decoupling makes both searches and inserts very efficient. Based on an actual implementation, we demonstrate that SETI clearly outperforms two previously proposed trajectory indexing mechanisms, namely the 3D R-tree and the TB-tree. Unlike previously proposed trajectory indexing structures, SETI is a logical indexing structure that uses existing spatial indexing structures, such as R-trees, without any modifications. Consequently, DBMSs that currently support R-trees can easily implement SETI, making it a both a practical and an efficient choice for indexing trajectory data sets.
@inproceedings {DBLP:conf/cidr/ChakkaEP03, author = {V. Prasad Chakka and Adam Everspaugh and Jignesh M. Patel}, booktitle = {CIDR}, title = {Indexing Large Trajectory Data Sets With SETI.}, year = {2003}, url = {db/conf/cidr/cidr2003.html#ChakkaEP03}, ee = {http://www-db.cs.wisc.edu/cidr/program/p15.pdf}, bibsource = {DBLP, http://dblp.uni-trier.de} }  ©2004 Association for Computing Machinery |
