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 -