Finding Similar Movements in Positional Data Streams
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-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 language | English |
|---|---|
| Title of host publication | Machine Learning and Data Mining for Sports Analytics - MLSA 2013 : Proceedings |
| Editors | Davis Jesse, Jan Van Haaren, Albrecht Zimmermann |
| Number of pages | 9 |
| Place of Publication | Prag |
| Publisher | Sun Site Central Europe (RWTH Aachen University) |
| Publication date | 2013 |
| Pages | 49-57 |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013 - Prag, Czech Republic Duration: 23.09.2013 → 27.09.2013 http://www.ecmlpkdd2013.org/ |
- Informatics
- Business informatics