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Mining positional data streams

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

Standard

Mining positional data streams. / Haase, Jens; Brefeld, Ulf.
New Frontiers in Mining Complex Patterns. Springer Verlag, 2015. p. 102-116.

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

Harvard

Haase, J & Brefeld, U 2015, Mining positional data streams. in New Frontiers in Mining Complex Patterns. Springer Verlag, pp. 102-116, 3rd International Workshop on New Frontiers in Mining Complex Patterns - NFMCP 2014, Nancy, France, 19.09.14. https://doi.org/10.1007/978-3-319-17876-9_7

APA

Haase, J., & Brefeld, U. (2015). Mining positional data streams. In New Frontiers in Mining Complex Patterns (pp. 102-116). Springer Verlag. https://doi.org/10.1007/978-3-319-17876-9_7

Vancouver

Haase J, Brefeld U. Mining positional data streams. In New Frontiers in Mining Complex Patterns. Springer Verlag. 2015. p. 102-116 doi: 10.1007/978-3-319-17876-9_7

Bibtex

@inbook{a4f6db1289c04684bd8c28473bb637bf,
title = "Mining positional data streams",
abstract = "We study frequent pattern mining from positional data streams. Existing approaches require discretised data to identify atomic events and are not applicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline.",
keywords = "Informatics, Pattern Mining, Dynamic Time Warping, Positional Data, Frequent Episode, Event Stream, Business informatics",
author = "Jens Haase and Ulf Brefeld",
year = "2015",
doi = "10.1007/978-3-319-17876-9_7",
language = "English",
isbn = "978-3-319-17875-2",
pages = "102--116",
booktitle = "New Frontiers in Mining Complex Patterns",
publisher = "Springer Verlag",
address = "Germany",
note = "3rd International Workshop on New Frontiers in Mining Complex Patterns - NFMCP 2014, NFMCP 2014 ; Conference date: 19-09-2014 Through 19-09-2014",

}

RIS

TY - CHAP

T1 - Mining positional data streams

AU - Haase, Jens

AU - Brefeld, Ulf

N1 - Conference code: 3

PY - 2015

Y1 - 2015

N2 - We study frequent pattern mining from positional data streams. Existing approaches require discretised data to identify atomic events and are not applicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline.

AB - We study frequent pattern mining from positional data streams. Existing approaches require discretised data to identify atomic events and are not applicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline.

KW - Informatics

KW - Pattern Mining

KW - Dynamic Time Warping

KW - Positional Data

KW - Frequent Episode

KW - Event Stream

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84931056379&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-17876-9_7

DO - 10.1007/978-3-319-17876-9_7

M3 - Article in conference proceedings

AN - SCOPUS:84931056379

SN - 978-3-319-17875-2

SP - 102

EP - 116

BT - New Frontiers in Mining Complex Patterns

PB - Springer Verlag

T2 - 3rd International Workshop on New Frontiers in Mining Complex Patterns - NFMCP 2014

Y2 - 19 September 2014 through 19 September 2014

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

Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items

Bengs, D., Brefeld, U., Kroehne, U. & Zehner, F., 2025, (Accepted/In press) In: Psychometrika.

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

Self-improvement for Computerized Adaptive Testing

Rudolph, Y., Neubauer, K. & Brefeld, U., 2026, Machine Learning and Knowledge Discovery in Databases - Research Track: European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings. Ribeiro, R. P., Pfahringer, B., Japkowicz, N., Larrañaga, P., Jorge, A. M., Soares, C., Abreu, P. H. & Gama, J. (eds.). Cham: Springer International Publishing, Vol. 2. p. 70-86 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence); vol. 16014).

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

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