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A Framework for Data Collection, Transformation and Processing in Industrial Systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationProceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages707-712
Number of pages6
ISBN (Electronic)9781538648292
DOIs
Publication statusPublished - 24 Sep 2018
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Industrial Informatics - Porto, Portugal
Duration: 18 Jul 201820 Jul 2018

Publication series

NameIEEE International Conference on Industrial Informatics
ISSN (Electronic)2161-2064

Conference

ConferenceIEEE International Conference on Industrial Informatics
CountryPortugal
CityPorto
Period18/07/1820/07/18

Abstract

Data collection requires homogenization of the data prior to processing it. This can create a challenge to the companies since data have varicose formats and schemas. This paper discusses the implementation of data collection framework using the PLANTCockpit open source platform, which is an integration platform for business processes. The framework is extended to fetch data from heterogeneous sources and then, allow the user to select the relevant data that matches his/her needs. In addition to this, the extendable framework also makes it possible to select the output data structure based on the user's requirements. Defining such a framework can reduce company's efforts for reshaping and modifying their architectures to handle new challenges posed by the ever-changing data. The framework not only restricts one to collection and transformation, but also provides an option to perform available processing techniques on the transformed data structure. The proposed framework has been tested on a cloud-based platform provided by the Cloud Collaborative Manufacturing Networks (C2NET) project.

Keywords

  • Data acquisition, Data Processing, Enterprise resource planning, Web services

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