A taxonomy for combining activity recognition and process discovery in industrial environments

Felix Mannhardt; Riccardo Bovo; Manuel Fradinho Oliveira; Simon Julier

doi:10.1007/978-3-030-03496-2_10

A taxonomy for combining activity recognition and process discovery in industrial environments

Felix Mannhardt, Riccardo Bovo, Manuel Fradinho Oliveira, Simon Julier

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

19 Citations (SciVal)

Abstract

Despite the increasing automation levels in an Industry 4.0 scenario, the tacit knowledge of highly skilled manufacturing workers remains of strategic importance. Retaining this knowledge by formally capturing it is a challenge for industrial organisations. This paper explores research on automatically capturing this knowledge by using methods from activity recognition and process mining on data obtained from sensorised workers and environments. Activity recognition lifts the abstraction level of sensor data to recognizable activities and process mining methods discover models of process executions. We classify the existing work, which largely neglects the possibility of applying process mining, and derive a taxonomy that identifies challenges and research gaps.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
Editors	David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros, Hujun Yin
Publisher	Springer
Pages	84-93
Number of pages	10
ISBN (Print)	9783030034955
DOIs	https://doi.org/10.1007/978-3-030-03496-2_10
Publication status	Published - 2018
Externally published	Yes
Event	19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain Duration: 21 Nov 2018 → 23 Nov 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11315 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Country/Territory	Spain
City	Madrid
Period	21/11/18 → 23/11/18

Keywords

Activity recognition
Industrial environment
Literature overview
Manufacturing
Process mining
Tacit knowledge

Access to Document

10.1007/978-3-030-03496-2_10

Cite this

Mannhardt, F., Bovo, R., Oliveira, M. F., & Julier, S. (2018). A taxonomy for combining activity recognition and process discovery in industrial environments. In D. Camacho, P. Novais, A. J. Tallón-Ballesteros, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings (pp. 84-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11315 LNCS). Springer. https://doi.org/10.1007/978-3-030-03496-2_10

Mannhardt, Felix ; Bovo, Riccardo ; Oliveira, Manuel Fradinho et al. / A taxonomy for combining activity recognition and process discovery in industrial environments. Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. editor / David Camacho ; Paulo Novais ; Antonio J. Tallón-Ballesteros ; Hujun Yin. Springer, 2018. pp. 84-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ab59fef6ca7749d98796137e4c0beb1c,

title = "A taxonomy for combining activity recognition and process discovery in industrial environments",

abstract = "Despite the increasing automation levels in an Industry 4.0 scenario, the tacit knowledge of highly skilled manufacturing workers remains of strategic importance. Retaining this knowledge by formally capturing it is a challenge for industrial organisations. This paper explores research on automatically capturing this knowledge by using methods from activity recognition and process mining on data obtained from sensorised workers and environments. Activity recognition lifts the abstraction level of sensor data to recognizable activities and process mining methods discover models of process executions. We classify the existing work, which largely neglects the possibility of applying process mining, and derive a taxonomy that identifies challenges and research gaps.",

keywords = "Activity recognition, Industrial environment, Literature overview, Manufacturing, Process mining, Tacit knowledge",

author = "Felix Mannhardt and Riccardo Bovo and Oliveira, {Manuel Fradinho} and Simon Julier",

year = "2018",

doi = "10.1007/978-3-030-03496-2_10",

language = "English",

isbn = "9783030034955",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "84--93",

editor = "David Camacho and Paulo Novais and Tall{\'o}n-Ballesteros, {Antonio J.} and Hujun Yin",

booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings",

address = "Germany",

note = "19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 ; Conference date: 21-11-2018 Through 23-11-2018",

}

Mannhardt, F, Bovo, R, Oliveira, MF & Julier, S 2018, A taxonomy for combining activity recognition and process discovery in industrial environments. in D Camacho, P Novais, AJ Tallón-Ballesteros & H Yin (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11315 LNCS, Springer, pp. 84-93, 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spain, 21/11/18. https://doi.org/10.1007/978-3-030-03496-2_10

A taxonomy for combining activity recognition and process discovery in industrial environments. / Mannhardt, Felix; Bovo, Riccardo; Oliveira, Manuel Fradinho et al.

Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. ed. / David Camacho; Paulo Novais; Antonio J. Tallón-Ballesteros; Hujun Yin. Springer, 2018. p. 84-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11315 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A taxonomy for combining activity recognition and process discovery in industrial environments

AU - Mannhardt, Felix

AU - Bovo, Riccardo

AU - Oliveira, Manuel Fradinho

AU - Julier, Simon

PY - 2018

Y1 - 2018

N2 - Despite the increasing automation levels in an Industry 4.0 scenario, the tacit knowledge of highly skilled manufacturing workers remains of strategic importance. Retaining this knowledge by formally capturing it is a challenge for industrial organisations. This paper explores research on automatically capturing this knowledge by using methods from activity recognition and process mining on data obtained from sensorised workers and environments. Activity recognition lifts the abstraction level of sensor data to recognizable activities and process mining methods discover models of process executions. We classify the existing work, which largely neglects the possibility of applying process mining, and derive a taxonomy that identifies challenges and research gaps.

AB - Despite the increasing automation levels in an Industry 4.0 scenario, the tacit knowledge of highly skilled manufacturing workers remains of strategic importance. Retaining this knowledge by formally capturing it is a challenge for industrial organisations. This paper explores research on automatically capturing this knowledge by using methods from activity recognition and process mining on data obtained from sensorised workers and environments. Activity recognition lifts the abstraction level of sensor data to recognizable activities and process mining methods discover models of process executions. We classify the existing work, which largely neglects the possibility of applying process mining, and derive a taxonomy that identifies challenges and research gaps.

KW - Activity recognition

KW - Industrial environment

KW - Literature overview

KW - Manufacturing

KW - Process mining

KW - Tacit knowledge

UR - http://www.scopus.com/inward/record.url?scp=85057070303&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-03496-2_10

DO - 10.1007/978-3-030-03496-2_10

M3 - Conference contribution

AN - SCOPUS:85057070303

SN - 9783030034955

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 84

EP - 93

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings

A2 - Camacho, David

A2 - Novais, Paulo

A2 - Tallón-Ballesteros, Antonio J.

A2 - Yin, Hujun

PB - Springer

T2 - 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018

Y2 - 21 November 2018 through 23 November 2018

ER -

Mannhardt F, Bovo R, Oliveira MF, Julier S. A taxonomy for combining activity recognition and process discovery in industrial environments. In Camacho D, Novais P, Tallón-Ballesteros AJ, Yin H, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Springer. 2018. p. 84-93. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-03496-2_10

A taxonomy for combining activity recognition and process discovery in industrial environments

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Best Workshop Paper at 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

Cite this

A taxonomy for combining activity recognition and process discovery in industrial environments

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Prizes

Best Workshop Paper at 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

Cite this