Manufacturing process data analysis pipelines: a requirements analysis and survey

Abstract Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big...
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Autor*in:	Ahmed Ismail [verfasserIn] Hong-Linh Truong [verfasserIn] Wolfgang Kastner [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Big data Smart manufacturing Industry 4.0 Analysis pipelines Industrial Internet of Things Data-driven decision making

Übergeordnetes Werk:	In: Journal of Big Data - SpringerOpen, 2015, 6(2019), 1, Seite 26
Übergeordnetes Werk:	volume:6 ; year:2019 ; number:1 ; pages:26

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s40537-018-0162-3

Katalog-ID:	DOAJ07137390X

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spelling	10.1186/s40537-018-0162-3 doi (DE-627)DOAJ07137390X (DE-599)DOAJecfa42eaf2184c5fbad1f3883b711067 DE-627 ger DE-627 rakwb eng TK7885-7895 T58.5-58.64 QA75.5-76.95 Ahmed Ismail verfasserin aut Manufacturing process data analysis pipelines: a requirements analysis and survey 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline. Big data Smart manufacturing Industry 4.0 Analysis pipelines Industrial Internet of Things Data-driven decision making Computer engineering. Computer hardware Information technology Electronic computers. Computer science Hong-Linh Truong verfasserin aut Wolfgang Kastner verfasserin aut In Journal of Big Data SpringerOpen, 2015 6(2019), 1, Seite 26 (DE-627)79213219X (DE-600)2780218-8 21961115 nnns volume:6 year:2019 number:1 pages:26 https://doi.org/10.1186/s40537-018-0162-3 kostenfrei https://doaj.org/article/ecfa42eaf2184c5fbad1f3883b711067 kostenfrei http://link.springer.com/article/10.1186/s40537-018-0162-3 kostenfrei https://doaj.org/toc/2196-1115 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 1 26
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callnumber-first	T - Technology
author	Ahmed Ismail
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title	Manufacturing process data analysis pipelines: a requirements analysis and survey
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title_sort	manufacturing process data analysis pipelines: a requirements analysis and survey
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title_auth	Manufacturing process data analysis pipelines: a requirements analysis and survey
abstract	Abstract Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline.
abstractGer	Abstract Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline.
abstract_unstemmed	Abstract Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline.
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