Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Comparison of Distance Measures for Planar Curves
Algorithmica
Addressing the Need for Map-Matching Speed: Localizing Globalb Curve-Matching Algorithms
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Fréchet distance for curves, revisited
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Exact algorithms for partial curve matching via the Fréchet distance
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Approximately matching polygonal curves with respect to the Fréchet distance
Computational Geometry: Theory and Applications - Special issue on the 19th European workshop on computational geometry - EuroCG 03
Proceedings of the twenty-sixth annual symposium on Computational geometry
Approximating the Fréchet distance for realistic curves in near linear time
Proceedings of the twenty-sixth annual symposium on Computational geometry
Constrained free space diagrams: a tool for trajectory analysis
International Journal of Geographical Information Science
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Inspired by video analysis of team sports, we study the following problem. Let P be a polygonal path in the plane with n vertices. We want to preprocess P into a data structure that can quickly count the number of inclusion-minimal subpaths of P whose Fréchet Distance to a given query segment Q is at most some threshold value ε. We present a data structure that solves an approximate version of this problem: it counts all subpaths whose Fréchet Distance is at most ε, but this count may also include subpaths whose Fréchet Distance is up to $(2+3\sqrt{2})\varepsilon $. For any parameter n≤s≤n2, our data structure can be tuned such that it uses O(s polylog n) storage and has $O((n/\sqrt{s}){\rm polylog} n)$ query time. For the special case where we wish to count all subpaths whose Fréchet Distance to Q is at most ε·length(Q), we present a structure with O(n polylog n) storage and O(polylog n) query time.