An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data

Hendrik Walzel; Milan Vathoopan; Alois Zoitl; Alois Knoll

doi:10.1109/ETFA.2019.8869083

An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data

Hendrik Walzel, Milan Vathoopan, Alois Zoitl, Alois Knoll

LIT Cyber-Physical Systems Lab

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

Abstract

Technological progress leads to an increased utilization of data analysis and Business Intelligence that support manufacturing management decisions. Many promising solutions utilize semantic technologies. However, the deployment and maintenance of semantic technologies especially in reconfigurable manufacturing environments require a lot of manual effort. Concepts to embed them in an automated environment, as required by Reconfigurable Manufacturing Systems, are limited. In this paper, we present an approach to reuse systems engineering data to guide an automated process that updates a production data knowledge base. Thereby, an ontology that integrates distributed operational data to compute Key Performance Indicators such as the Overall Equipment Effectiveness index can be updated during the manufacturing reconfiguration process. This reduces the effort to handle the required changes of semantic data integration systems and enables a cost-effective adaption of the Business Intelligence for Reconfigurable Manufacturing Systems.

Original language	English
Title of host publication	2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Pages	1693-1696
Number of pages	4
DOIs	https://doi.org/10.1109/ETFA.2019.8869083
Publication status	Published - Sept 2019

Publication series

Name	Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation)
ISSN (Print)	1946-0740

Fields of science

202017 Embedded systems
102022 Software development
102025 Distributed systems
102029 Practical computer science
202003 Automation
202041 Computer engineering

JKU Focus areas

Digital Transformation

UN SDGs

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

Access to Document

10.1109/ETFA.2019.8869083

Cite this

Walzel, H., Vathoopan, M., Zoitl, A., & Knoll, A. (2019). An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data. In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1693-1696). Article 8869083 (Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation)). https://doi.org/10.1109/ETFA.2019.8869083

@inproceedings{dba00ea6bdb6417aa223a9d390bdc3a0,

title = "An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data",

abstract = "Technological progress leads to an increased utilization of data analysis and Business Intelligence that support manufacturing management decisions. Many promising solutions utilize semantic technologies. However, the deployment and maintenance of semantic technologies especially in reconfigurable manufacturing environments require a lot of manual effort. Concepts to embed them in an automated environment, as required by Reconfigurable Manufacturing Systems, are limited. In this paper, we present an approach to reuse systems engineering data to guide an automated process that updates a production data knowledge base. Thereby, an ontology that integrates distributed operational data to compute Key Performance Indicators such as the Overall Equipment Effectiveness index can be updated during the manufacturing reconfiguration process. This reduces the effort to handle the required changes of semantic data integration systems and enables a cost-effective adaption of the Business Intelligence for Reconfigurable Manufacturing Systems.",

author = "Hendrik Walzel and Milan Vathoopan and Alois Zoitl and Alois Knoll",

year = "2019",

month = sep,

doi = "10.1109/ETFA.2019.8869083",

language = "English",

series = "Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation)",

pages = "1693--1696",

booktitle = "2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)",

}

Walzel, H, Vathoopan, M, Zoitl, A & Knoll, A 2019, An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data. in 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)., 8869083, Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation), pp. 1693-1696. https://doi.org/10.1109/ETFA.2019.8869083

An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data. / Walzel, Hendrik; Vathoopan, Milan; Zoitl, Alois et al.
2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 2019. p. 1693-1696 8869083 (Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation)).

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

TY - GEN

T1 - An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data

AU - Walzel, Hendrik

AU - Vathoopan, Milan

AU - Zoitl, Alois

AU - Knoll, Alois

PY - 2019/9

Y1 - 2019/9

N2 - Technological progress leads to an increased utilization of data analysis and Business Intelligence that support manufacturing management decisions. Many promising solutions utilize semantic technologies. However, the deployment and maintenance of semantic technologies especially in reconfigurable manufacturing environments require a lot of manual effort. Concepts to embed them in an automated environment, as required by Reconfigurable Manufacturing Systems, are limited. In this paper, we present an approach to reuse systems engineering data to guide an automated process that updates a production data knowledge base. Thereby, an ontology that integrates distributed operational data to compute Key Performance Indicators such as the Overall Equipment Effectiveness index can be updated during the manufacturing reconfiguration process. This reduces the effort to handle the required changes of semantic data integration systems and enables a cost-effective adaption of the Business Intelligence for Reconfigurable Manufacturing Systems.

AB - Technological progress leads to an increased utilization of data analysis and Business Intelligence that support manufacturing management decisions. Many promising solutions utilize semantic technologies. However, the deployment and maintenance of semantic technologies especially in reconfigurable manufacturing environments require a lot of manual effort. Concepts to embed them in an automated environment, as required by Reconfigurable Manufacturing Systems, are limited. In this paper, we present an approach to reuse systems engineering data to guide an automated process that updates a production data knowledge base. Thereby, an ontology that integrates distributed operational data to compute Key Performance Indicators such as the Overall Equipment Effectiveness index can be updated during the manufacturing reconfiguration process. This reduces the effort to handle the required changes of semantic data integration systems and enables a cost-effective adaption of the Business Intelligence for Reconfigurable Manufacturing Systems.

U2 - 10.1109/ETFA.2019.8869083

DO - 10.1109/ETFA.2019.8869083

M3 - Conference proceedings

T3 - Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation)

SP - 1693

EP - 1696

BT - 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

ER -

Walzel H, Vathoopan M, Zoitl A, Knoll A. An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data. In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 2019. p. 1693-1696. 8869083. (Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation)). doi: 10.1109/ETFA.2019.8869083