Finding Similar Movements in Positional Data Streams

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

In this paper, we study the problem of efficiently finding similar movements in positional data streams, given a query trajectory. Our approach is based on a translation-, rotation-, and scale-invariant representation of movements. Near-neighbours given a query trajectory are then efficiently computed using dynamic time warping and locality sensitive hashing. Empirically, we show the efficiency and accuracy of our approach on positional data streams recorded from a real soccer game.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining for Sports Analytics - MLSA 2013 : Proceedings
EditorsDavis Jesse, Jan Van Haaren, Albrecht Zimmermann
Number of pages9
Place of PublicationPrag
PublisherSun Site Central Europe (RWTH Aachen University)
Publication date2013
Pages49-57
Publication statusPublished - 2013
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013 - Prag, Czech Republic
Duration: 23.09.201327.09.2013
http://www.ecmlpkdd2013.org/

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