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Finding Similar Movements in Positional Data Streams

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

Finding Similar Movements in Positional Data Streams. / Haase, Jens; Brefeld, Ulf.
Machine Learning and Data Mining for Sports Analytics - MLSA 2013: Proceedings. ed. / Davis Jesse; Jan Van Haaren; Albrecht Zimmermann. Prag: Sun Site Central Europe (RWTH Aachen University), 2013. p. 49-57 (CEUR Workshop Proceedings; No. 1969).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Haase, J & Brefeld, U 2013, Finding Similar Movements in Positional Data Streams. in D Jesse, J Van Haaren & A Zimmermann (eds), Machine Learning and Data Mining for Sports Analytics - MLSA 2013: Proceedings. CEUR Workshop Proceedings, no. 1969, Sun Site Central Europe (RWTH Aachen University), Prag, pp. 49-57, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013, Prag, Czech Republic, 23.09.13. <http://www.ecmlpkdd2013.org/wp-content/uploads/2013/09/mlsa13_submission_13.pdf>

APA

Haase, J., & Brefeld, U. (2013). Finding Similar Movements in Positional Data Streams. In D. Jesse, J. Van Haaren, & A. Zimmermann (Eds.), Machine Learning and Data Mining for Sports Analytics - MLSA 2013: Proceedings (pp. 49-57). (CEUR Workshop Proceedings; No. 1969). Sun Site Central Europe (RWTH Aachen University). http://www.ecmlpkdd2013.org/wp-content/uploads/2013/09/mlsa13_submission_13.pdf

Vancouver

Haase J, Brefeld U. Finding Similar Movements in Positional Data Streams. In Jesse D, Van Haaren J, Zimmermann A, editors, Machine Learning and Data Mining for Sports Analytics - MLSA 2013: Proceedings. Prag: Sun Site Central Europe (RWTH Aachen University). 2013. p. 49-57. (CEUR Workshop Proceedings; 1969).

Bibtex

@inbook{f3b5a32bf1784fb39a85a24c95211d75,
title = "Finding Similar Movements in Positional Data Streams",
abstract = "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.",
keywords = "Informatics, Business informatics",
author = "Jens Haase and Ulf Brefeld",
year = "2013",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "Sun Site Central Europe (RWTH Aachen University)",
number = "1969",
pages = "49--57",
editor = "Davis Jesse and {Van Haaren}, Jan and Albrecht Zimmermann",
booktitle = "Machine Learning and Data Mining for Sports Analytics - MLSA 2013",
address = "Germany",
note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013, ECMLPKDD 2013 ; Conference date: 23-09-2013 Through 27-09-2013",
url = "http://www.ecmlpkdd2013.org/",

}

RIS

TY - CHAP

T1 - Finding Similar Movements in Positional Data Streams

AU - Haase, Jens

AU - Brefeld, Ulf

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85007206303&partnerID=8YFLogxK

M3 - Article in conference proceedings

T3 - CEUR Workshop Proceedings

SP - 49

EP - 57

BT - Machine Learning and Data Mining for Sports Analytics - MLSA 2013

A2 - Jesse, Davis

A2 - Van Haaren, Jan

A2 - Zimmermann, Albrecht

PB - Sun Site Central Europe (RWTH Aachen University)

CY - Prag

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013

Y2 - 23 September 2013 through 27 September 2013

ER -

Other publications by the same author(s)

Interactive sequential generative models for team sports

Fassmeyer, D., Cordes, M. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 15 p., 38.

Research output: Journal contributions › Journal articles › Research › peer-review

Machine Learning and Data Mining for Sports Analytics: 11th International Workshop, MLSA 2024, Vilnius, Lithuania, September 9, 2024, Revised Selected Papers

Brefeld, U. (Editor), Davis, J. (Editor), Van Haaren, J. (Editor) & Zimmermann, A. (Editor), 2025, Cham: Springer Verlag. 119 p. (Communications in Computer and Information Science; vol. 2460)

Research output: Books and anthologies › Conference proceedings › Research

Masked autoencoder for multiagent trajectories

Rudolph, Y. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 18 p., 44.

Research output: Journal contributions › Journal articles › Research › peer-review

The promise and challenges of computer mouse trajectories in DMHIs – A feasibility study on pre-treatment dropout predictions

Zantvoort, K., Matthiesen, J., Bjurner, P., Bendix, M., Brefeld, U., Funk, B. & Kaldo, V., 06.2025, In: Internet Interventions. 40, 7 p., 100828.

Research output: Journal contributions › Journal articles › Research › peer-review

Toward Learning Distributions of Distributions

Wohlstein, M. & Brefeld, U., 2025, In: Proceedings of Machine Learning Research. 265, p. 269-275 7 p.

Research output: Journal contributions › Conference article in journal › Research › peer-review

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