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:	Ismail, Ahmed [verfasserIn] Truong, Hong-Linh Kastner, Wolfgang

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 High performance computing

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: Journal of Big Data - Berlin : SpringerOpen, 2014, 6(2019), 1 vom: 07. Jan.
Übergeordnetes Werk:	volume:6 ; year:2019 ; number:1 ; day:07 ; month:01

Links:	Volltext

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

Katalog-ID:	SPR036631728

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spelling	10.1186/s40537-018-0162-3 doi (DE-627)SPR036631728 (SPR)s40537-018-0162-3-e DE-627 ger DE-627 rakwb eng Ismail, Ahmed verfasserin (orcid)0000-0003-3472-0738 aut Manufacturing process data analysis pipelines: a requirements analysis and survey 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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 (dpeaa)DE-He213 Smart manufacturing (dpeaa)DE-He213 Industry 4.0 (dpeaa)DE-He213 Analysis pipelines (dpeaa)DE-He213 Industrial Internet of Things (dpeaa)DE-He213 Data-driven decision making (dpeaa)DE-He213 High performance computing (dpeaa)DE-He213 Truong, Hong-Linh aut Kastner, Wolfgang aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 07. Jan. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:07 month:01 https://dx.doi.org/10.1186/s40537-018-0162-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 07 01
allfields_unstemmed	10.1186/s40537-018-0162-3 doi (DE-627)SPR036631728 (SPR)s40537-018-0162-3-e DE-627 ger DE-627 rakwb eng Ismail, Ahmed verfasserin (orcid)0000-0003-3472-0738 aut Manufacturing process data analysis pipelines: a requirements analysis and survey 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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 (dpeaa)DE-He213 Smart manufacturing (dpeaa)DE-He213 Industry 4.0 (dpeaa)DE-He213 Analysis pipelines (dpeaa)DE-He213 Industrial Internet of Things (dpeaa)DE-He213 Data-driven decision making (dpeaa)DE-He213 High performance computing (dpeaa)DE-He213 Truong, Hong-Linh aut Kastner, Wolfgang aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 07. Jan. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:07 month:01 https://dx.doi.org/10.1186/s40537-018-0162-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 07 01
allfieldsGer	10.1186/s40537-018-0162-3 doi (DE-627)SPR036631728 (SPR)s40537-018-0162-3-e DE-627 ger DE-627 rakwb eng Ismail, Ahmed verfasserin (orcid)0000-0003-3472-0738 aut Manufacturing process data analysis pipelines: a requirements analysis and survey 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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 (dpeaa)DE-He213 Smart manufacturing (dpeaa)DE-He213 Industry 4.0 (dpeaa)DE-He213 Analysis pipelines (dpeaa)DE-He213 Industrial Internet of Things (dpeaa)DE-He213 Data-driven decision making (dpeaa)DE-He213 High performance computing (dpeaa)DE-He213 Truong, Hong-Linh aut Kastner, Wolfgang aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 07. Jan. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:07 month:01 https://dx.doi.org/10.1186/s40537-018-0162-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 07 01
allfieldsSound	10.1186/s40537-018-0162-3 doi (DE-627)SPR036631728 (SPR)s40537-018-0162-3-e DE-627 ger DE-627 rakwb eng Ismail, Ahmed verfasserin (orcid)0000-0003-3472-0738 aut Manufacturing process data analysis pipelines: a requirements analysis and survey 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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 (dpeaa)DE-He213 Smart manufacturing (dpeaa)DE-He213 Industry 4.0 (dpeaa)DE-He213 Analysis pipelines (dpeaa)DE-He213 Industrial Internet of Things (dpeaa)DE-He213 Data-driven decision making (dpeaa)DE-He213 High performance computing (dpeaa)DE-He213 Truong, Hong-Linh aut Kastner, Wolfgang aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 07. Jan. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:07 month:01 https://dx.doi.org/10.1186/s40537-018-0162-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 07 01
language	English
source	Enthalten in Journal of Big Data 6(2019), 1 vom: 07. Jan. volume:6 year:2019 number:1 day:07 month:01
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institution	findex.gbv.de
topic_facet	Big data Smart manufacturing Industry 4.0 Analysis pipelines Industrial Internet of Things Data-driven decision making High performance computing
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author	Ismail, Ahmed
spellingShingle	Ismail, Ahmed misc Big data misc Smart manufacturing misc Industry 4.0 misc Analysis pipelines misc Industrial Internet of Things misc Data-driven decision making misc High performance computing Manufacturing process data analysis pipelines: a requirements analysis and survey
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topic_title	Manufacturing process data analysis pipelines: a requirements analysis and survey Big data (dpeaa)DE-He213 Smart manufacturing (dpeaa)DE-He213 Industry 4.0 (dpeaa)DE-He213 Analysis pipelines (dpeaa)DE-He213 Industrial Internet of Things (dpeaa)DE-He213 Data-driven decision making (dpeaa)DE-He213 High performance computing (dpeaa)DE-He213
topic	misc Big data misc Smart manufacturing misc Industry 4.0 misc Analysis pipelines misc Industrial Internet of Things misc Data-driven decision making misc High performance computing
topic_unstemmed	misc Big data misc Smart manufacturing misc Industry 4.0 misc Analysis pipelines misc Industrial Internet of Things misc Data-driven decision making misc High performance computing
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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
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. © The Author(s) 2019
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. © The Author(s) 2019
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. © The Author(s) 2019
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title_short	Manufacturing process data analysis pipelines: a requirements analysis and survey
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