Enabling Process Mining on Sensor Data from Smart Products

M.L. Van Eck; N. Sidorova; W.M.P. Van Der Aalst

doi:10.1109/RCIS.2016.7549355

Enabling Process Mining on Sensor Data from Smart Products

M.L. Van Eck, N. Sidorova, W.M.P. Van Der Aalst

Eindhoven MedTech Innovation Center

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

12 Citations (Scopus)

315 Downloads (Pure)

Abstract

In this paper we address the challenge of applying process mining to discover models of human behaviour from sensor data. This challenge is caused by a gap between sensor data and the event logs that are used as input for process mining techniques, so we provide a transformation approach to bridge this gap. As a result, besides the automatic discovery of process models, the transformed sensor data can also be used by various other process mining techniques, e.g. to identify differences between observed behaviour and expected behaviour. We discuss the transformation approach in the context of the design process of smart products and related services, using a case study performed at Philips where a smart baby bottle has been developed. This case study also demonstrates that the use of process mining can add value to the smart product design process.

Original language	English
Title of host publication	IEEE RCIS 2016 - IEEE 10th International Conference on Research Challenges in Information Science, 1-3 may 2016, Grenoble, France
Place of Publication	Brussels
Publisher	IEEE Computer Society
Pages	1-12
Number of pages	12
ISBN (Electronic)	978-1-4799-8710-8
ISBN (Print)	978-1-4799-8711-5
DOIs	https://doi.org/10.1109/RCIS.2016.7549355
Publication status	Published - 23 Aug 2016
Event	10th IEEE International Conference on Research Challenges in Information Science, IEEE RCIS 2016 - Grenoble, France Duration: 1 May 2016 → 3 May 2016

Conference

Conference	10th IEEE International Conference on Research Challenges in Information Science, IEEE RCIS 2016
Country	France
City	Grenoble
Period	1/05/16 → 3/05/16

Keywords

activity recognition
process mining
product design
sensor data
smart products

Access to Document

10.1109/RCIS.2016.7549355

EnablingProcessMiningOnSensorDataFinal author version , 929 KB

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1 Finished

Impuls I - Health - Perinatal Monitoring - TU/e funded
1/01/15 → 31/12/20
Project: Research indirect

Perinatal Medicine
M.B. (Beatrijs) van der Hout-van der Jagt (Content manager) & Eugenie Delvaux (Content manager)
Impact: Research Topic/Theme (at group level)

Cite this

@inproceedings{37d16e0a2a5943b79dbd3891dec71397,

title = "Enabling Process Mining on Sensor Data from Smart Products",

abstract = "In this paper we address the challenge of applying process mining to discover models of human behaviour from sensor data. This challenge is caused by a gap between sensor data and the event logs that are used as input for process mining techniques, so we provide a transformation approach to bridge this gap. As a result, besides the automatic discovery of process models, the transformed sensor data can also be used by various other process mining techniques, e.g. to identify differences between observed behaviour and expected behaviour. We discuss the transformation approach in the context of the design process of smart products and related services, using a case study performed at Philips where a smart baby bottle has been developed. This case study also demonstrates that the use of process mining can add value to the smart product design process.",

keywords = "activity recognition, process mining, product design, sensor data, smart products",

author = "{Van Eck}, M.L. and N. Sidorova and {Van Der Aalst}, W.M.P.",

year = "2016",

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publisher = "IEEE Computer Society",

address = "United States",

note = "10th IEEE International Conference on Research Challenges in Information Science, IEEE RCIS 2016 ; Conference date: 01-05-2016 Through 03-05-2016",

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Van Eck, ML , Sidorova, N & Van Der Aalst, WMP 2016, Enabling Process Mining on Sensor Data from Smart Products. in IEEE RCIS 2016 - IEEE 10th International Conference on Research Challenges in Information Science, 1-3 may 2016, Grenoble, France., 7549355, IEEE Computer Society, Brussels, pp. 1-12, 10th IEEE International Conference on Research Challenges in Information Science, IEEE RCIS 2016, Grenoble, France, 1/05/16. https://doi.org/10.1109/RCIS.2016.7549355

Enabling Process Mining on Sensor Data from Smart Products. / Van Eck, M.L.; Sidorova, N.; Van Der Aalst, W.M.P.

IEEE RCIS 2016 - IEEE 10th International Conference on Research Challenges in Information Science, 1-3 may 2016, Grenoble, France. Brussels : IEEE Computer Society, 2016. p. 1-12 7549355.

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

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AU - Sidorova, N.

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N2 - In this paper we address the challenge of applying process mining to discover models of human behaviour from sensor data. This challenge is caused by a gap between sensor data and the event logs that are used as input for process mining techniques, so we provide a transformation approach to bridge this gap. As a result, besides the automatic discovery of process models, the transformed sensor data can also be used by various other process mining techniques, e.g. to identify differences between observed behaviour and expected behaviour. We discuss the transformation approach in the context of the design process of smart products and related services, using a case study performed at Philips where a smart baby bottle has been developed. This case study also demonstrates that the use of process mining can add value to the smart product design process.

AB - In this paper we address the challenge of applying process mining to discover models of human behaviour from sensor data. This challenge is caused by a gap between sensor data and the event logs that are used as input for process mining techniques, so we provide a transformation approach to bridge this gap. As a result, besides the automatic discovery of process models, the transformed sensor data can also be used by various other process mining techniques, e.g. to identify differences between observed behaviour and expected behaviour. We discuss the transformation approach in the context of the design process of smart products and related services, using a case study performed at Philips where a smart baby bottle has been developed. This case study also demonstrates that the use of process mining can add value to the smart product design process.

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Enabling Process Mining on Sensor Data from Smart Products

Abstract

Conference

Keywords

Access to Document

Impuls I - Health - Perinatal Monitoring - TU/e funded

Perinatal Medicine

Cite this