Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes

Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral m...

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Published inMethodsX Vol. 10; p. 102124
Main Authors Jawad, M.S., Dhawale, Chitra, Ramli, Azizul Azhar Bin, Mahdin, Hairulnizam
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 01.01.2023
Elsevier
Subjects
Adoption of Best Development Practices of Enterprise Knowledge-Graph for scalable Manufacturing Processes
Data Analytics
Data Fabric
Enterprise knowledge graph
Machine learning
Method
Ontology
Smart manufacturing
Ontology
Machine learning
Data Analytics
Data Fabric
Adoption of Best Development Practices of Enterprise Knowledge-Graph for scalable Manufacturing Processes
Enterprise knowledge graph
Smart manufacturing
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Abstract Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects:•Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures.•Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises. [Display omitted]
AbstractList Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance.This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects: • Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures. • Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises.
Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects:•Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures.•Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises. [Display omitted]
Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects: • Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures. • Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises. Image, graphical abstract
ArticleNumber 102124
Author Ramli, Azizul Azhar Bin
Mahdin, Hairulnizam
Dhawale, Chitra
Jawad, M.S.
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  orcidid: 0000-0003-1956-9638
  surname: Jawad
  fullname: Jawad, M.S.
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  givenname: Chitra
  surname: Dhawale
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Keywords Ontology
Machine learning
Data Analytics
Data Fabric
Adoption of Best Development Practices of Enterprise Knowledge-Graph for scalable Manufacturing Processes
Enterprise knowledge graph
Smart manufacturing
Language English
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Snippet Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing...
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SubjectTerms Adoption of Best Development Practices of Enterprise Knowledge-Graph for scalable Manufacturing Processes
Data Analytics
Data Fabric
Enterprise knowledge graph
Machine learning
Method
Ontology
Smart manufacturing
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Title Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes
URI https://dx.doi.org/10.1016/j.mex.2023.102124
https://www.ncbi.nlm.nih.gov/pubmed/36974325
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