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Robust and fast similarity search for moving object trajectories

Robust and fast similarity search for moving object trajectories Robust and Fast Similarity Search for Moving Object Trajectories ¨ Lei Chen, M. Tamer Ozsu University of Waterloo School of Computer Science {l6chen,tozsu}@uwaterloo.ca New Jersey Inst. of Technology Dept. of Computer Science Vincent Oria oria@homer.njit.edu of S and si is a vector of arity d (d usually equals 2 or 3) that is sampled at timestamp ti . Therefore, trajectories can be considered as two (x-y plane) or three (x-y-z plane) dimensional time series data. In terms of similarity-based retrieval, we are interested in the movement shape of the trajectories; sequences of sampled vectors are important in measuring the similarity between two trajectories and time components can be ignored. This separates similaritybased retrieval from queries in spatio-temporal databases where time components of trajectories are important to answer time slice or time interval queries [28]. Considerable research has been conducted on similarity-based retrieval on one-dimensional time series data, such as stock or commodity prices, sales volume, weather data and biomedical measurements (e.g. [1, 24, 20, 23, 40]). Unfortunately, the distance functions and indexing methods proposed for one-dimensional time series data can not be directly applied to moving object trajectories due to their unique characteristics. ¢ Trajectories are usually two http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Robust and fast similarity search for moving object trajectories

Association for Computing Machinery — Jun 14, 2005

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Datasource
Association for Computing Machinery
Copyright
Copyright © 2005 by ACM Inc.
ISBN
1-59593-060-4
doi
10.1145/1066157.1066213
Publisher site
See Article on Publisher Site

Abstract

Robust and Fast Similarity Search for Moving Object Trajectories ¨ Lei Chen, M. Tamer Ozsu University of Waterloo School of Computer Science {l6chen,tozsu}@uwaterloo.ca New Jersey Inst. of Technology Dept. of Computer Science Vincent Oria oria@homer.njit.edu of S and si is a vector of arity d (d usually equals 2 or 3) that is sampled at timestamp ti . Therefore, trajectories can be considered as two (x-y plane) or three (x-y-z plane) dimensional time series data. In terms of similarity-based retrieval, we are interested in the movement shape of the trajectories; sequences of sampled vectors are important in measuring the similarity between two trajectories and time components can be ignored. This separates similaritybased retrieval from queries in spatio-temporal databases where time components of trajectories are important to answer time slice or time interval queries [28]. Considerable research has been conducted on similarity-based retrieval on one-dimensional time series data, such as stock or commodity prices, sales volume, weather data and biomedical measurements (e.g. [1, 24, 20, 23, 40]). Unfortunately, the distance functions and indexing methods proposed for one-dimensional time series data can not be directly applied to moving object trajectories due to their unique characteristics. ¢ Trajectories are usually two

There are no references for this article.