Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes

Matthias Ehrendorfer; Juergen Mangler; Stefanie Rinderle-Ma

doi:10.1007/978-3-030-79108-7_4

Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes

Matthias Ehrendorfer
, Juergen Mangler
, Stefanie Rinderle-Ma

Informatics 17 - Chair of Information Systems and Business Process Management

Vienna-UNI
Technical University of Munich

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

4 Scopus citations

Abstract

One target of process analysis, monitoring, and prediction is the process outcome, e.g., the quality of a produced part. The process outcome is affected by process execution data, including (external) sensor data streams, e.g., indicating an overheating machine. Challenges are to select the “right” sensors –possibly a multitude of sensors is available– and to specify how the sensor data streams are aggregated and used to calculate the impact on the outcome. This paper introduces process task annotations to specify the selected sensors, their aggregation, and initial impact functions. The initial impact functions are then refined, e.g., threshold values and the impact of sensor data streams are determined. The approach is prototypically implemented. Its applicability is demonstrated based on a real-world manufacturing scenario.

Original language	English
Title of host publication	Intelligent Information Systems - CAiSE Forum 2021, Proceedings
Editors	Selmin Nurcan, Axel Korthaus
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	29-37
Number of pages	9
ISBN (Print)	9783030791070
DOIs	https://doi.org/10.1007/978-3-030-79108-7_4
State	Published - 2021
Event	CAiSE Forum 2021 held as part of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021 - Virtual, Online Duration: 28 Jun 2021 → 2 Jul 2021

Publication series

Name	Lecture Notes in Business Information Processing
Volume	424 LNBIP
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	CAiSE Forum 2021 held as part of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021
City	Virtual, Online
Period	28/06/21 → 2/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Process impact analysis
Process outcome
Sensor data streams

Access to Document

10.1007/978-3-030-79108-7_4

Cite this

Ehrendorfer, M., Mangler, J., & Rinderle-Ma, S. (2021). Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes. In S. Nurcan, & A. Korthaus (Eds.), Intelligent Information Systems - CAiSE Forum 2021, Proceedings (pp. 29-37). (Lecture Notes in Business Information Processing; Vol. 424 LNBIP). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-79108-7_4

Ehrendorfer, Matthias ; Mangler, Juergen ; Rinderle-Ma, Stefanie. / Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes. Intelligent Information Systems - CAiSE Forum 2021, Proceedings. editor / Selmin Nurcan ; Axel Korthaus. Springer Science and Business Media Deutschland GmbH, 2021. pp. 29-37 (Lecture Notes in Business Information Processing).

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title = "Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes",

abstract = "One target of process analysis, monitoring, and prediction is the process outcome, e.g., the quality of a produced part. The process outcome is affected by process execution data, including (external) sensor data streams, e.g., indicating an overheating machine. Challenges are to select the {\textquotedblleft}right{\textquotedblright} sensors {\textendash}possibly a multitude of sensors is available{\textendash} and to specify how the sensor data streams are aggregated and used to calculate the impact on the outcome. This paper introduces process task annotations to specify the selected sensors, their aggregation, and initial impact functions. The initial impact functions are then refined, e.g., threshold values and the impact of sensor data streams are determined. The approach is prototypically implemented. Its applicability is demonstrated based on a real-world manufacturing scenario.",

keywords = "Process impact analysis, Process outcome, Sensor data streams",

author = "Matthias Ehrendorfer and Juergen Mangler and Stefanie Rinderle-Ma",

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year = "2021",

doi = "10.1007/978-3-030-79108-7\_4",

language = "English",

isbn = "9783030791070",

series = "Lecture Notes in Business Information Processing",

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Ehrendorfer, M, Mangler, J & Rinderle-Ma, S 2021, Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes. in S Nurcan & A Korthaus (eds), Intelligent Information Systems - CAiSE Forum 2021, Proceedings. Lecture Notes in Business Information Processing, vol. 424 LNBIP, Springer Science and Business Media Deutschland GmbH, pp. 29-37, CAiSE Forum 2021 held as part of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021, Virtual, Online, 28/06/21. https://doi.org/10.1007/978-3-030-79108-7_4

Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes. / Ehrendorfer, Matthias; Mangler, Juergen; Rinderle-Ma, Stefanie.
Intelligent Information Systems - CAiSE Forum 2021, Proceedings. ed. / Selmin Nurcan; Axel Korthaus. Springer Science and Business Media Deutschland GmbH, 2021. p. 29-37 (Lecture Notes in Business Information Processing; Vol. 424 LNBIP).

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

TY - GEN

T1 - Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes

AU - Ehrendorfer, Matthias

AU - Mangler, Juergen

AU - Rinderle-Ma, Stefanie

PY - 2021

Y1 - 2021

N2 - One target of process analysis, monitoring, and prediction is the process outcome, e.g., the quality of a produced part. The process outcome is affected by process execution data, including (external) sensor data streams, e.g., indicating an overheating machine. Challenges are to select the “right” sensors –possibly a multitude of sensors is available– and to specify how the sensor data streams are aggregated and used to calculate the impact on the outcome. This paper introduces process task annotations to specify the selected sensors, their aggregation, and initial impact functions. The initial impact functions are then refined, e.g., threshold values and the impact of sensor data streams are determined. The approach is prototypically implemented. Its applicability is demonstrated based on a real-world manufacturing scenario.

AB - One target of process analysis, monitoring, and prediction is the process outcome, e.g., the quality of a produced part. The process outcome is affected by process execution data, including (external) sensor data streams, e.g., indicating an overheating machine. Challenges are to select the “right” sensors –possibly a multitude of sensors is available– and to specify how the sensor data streams are aggregated and used to calculate the impact on the outcome. This paper introduces process task annotations to specify the selected sensors, their aggregation, and initial impact functions. The initial impact functions are then refined, e.g., threshold values and the impact of sensor data streams are determined. The approach is prototypically implemented. Its applicability is demonstrated based on a real-world manufacturing scenario.

KW - Process impact analysis

KW - Process outcome

KW - Sensor data streams

UR - https://www.scopus.com/pages/publications/85111370622

U2 - 10.1007/978-3-030-79108-7_4

DO - 10.1007/978-3-030-79108-7_4

M3 - Conference contribution

AN - SCOPUS:85111370622

SN - 9783030791070

T3 - Lecture Notes in Business Information Processing

SP - 29

EP - 37

BT - Intelligent Information Systems - CAiSE Forum 2021, Proceedings

A2 - Nurcan, Selmin

A2 - Korthaus, Axel

PB - Springer Science and Business Media Deutschland GmbH

T2 - CAiSE Forum 2021 held as part of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021

Y2 - 28 June 2021 through 2 July 2021

ER -

Ehrendorfer M, Mangler J, Rinderle-Ma S. Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes. In Nurcan S, Korthaus A, editors, Intelligent Information Systems - CAiSE Forum 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 29-37. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-030-79108-7_4

Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this