Fast subsequence matching in time-series databases
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Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Aggregation and comparison of trajectories
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Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Rotation invariant distance measures for trajectories
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient k-nearest-neighbor search algorthims for historical moving object trajectories
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TS2-tree - an efficient similarity based organization for trajectory data
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Spatio-temporal similarity measure algorithm for moving objects on spatial networks
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Searching for similar trajectories on road networks using spatio-temporal similarity
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Visually exploring movement data via similarity-based analysis
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Dynamic k-means: a clustering technique for moving object trajectories
International Journal of Intelligent Information and Database Systems
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An interesting issue in moving objects databases is to find similar trajectories of moving objects. Previous work on this topic focuses on movement patterns (trajectories with time dimension) of moving objects, rather than spatial shapes (trajectories without time dimension) of their trajectories. In this paper we propose a simple and effective way to compare spatial shapes of moving object trajectories. We introduce a new distance function based on ``one way distance'' (OWD). Algorithms for evaluating OWD in both continuous (piece wise linear) and discrete (grid representation) cases are developed. An index structure for OWD in grid representation, which guarantees no false dismissals, is also given to improve the efficiency of similarity search. Empirical studies show that OWD out-performs existent methods not only in precision, but also in efficiency. And the results of OWD in continuous case can be approximated by discrete case efficiently.