Big und Smart Data

Folmer, Jens; Kirchen, Iris; Trunzer, Emanuel; Vogel‐Heuser, Birgit; Pötter, Thorsten; Graube, Markus; Heinze, Sebastian; Urbas, Leon; Atzmüller, Martin; Arnu, David

doi:10.17560/atp.v59i01-02.623

Cited by 8 publications

(3 citation statements)

References 10 publications

(3 reference statements)

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“…Much research effort is put into semantic description of data to make it machinereadable and especially -understandable. Efforts are going in different directions using linked data approaches [38,39], description of devices like the Industry 4.0 reference architecture (RAMI 4.0) and similar device description approaches [40,41], and various other ways to describe sensors and connected machines [42][43][44]. The most promising seems to use semantics already designed in OPC UA.…”

Section: Semantic Description Of Available Datamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

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Section: Semantic Description Of Available Datamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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“…However, the term “Big Data” as a prerequisite for data‐driven evaluation procedures is not appropriate for the process industry, because even with quantitatively large data sets, information is typically only available for campaigns about a few batches with a series of measurement data that do not exhibit sufficient variance for a data‐driven evaluation – not comparable with the data sets on the WWW or from large internet groups. 22, 23…”

Section: Advantages and Challenges Of Modular Chemical Productionmentioning

confidence: 99%

“…However, the term ''Big Data'' as a prerequisite for data-driven evaluation procedures is not appropriate for the process industry, because even with quantitatively large data sets, information is typically only available for campaigns about a few batches with a series of measurement data that do not exhibit sufficient variance for a data-driven evaluation -not comparable with the data sets on the WWW or from large internet groups. [22,23] In the process technology environment, the term ''Smart Data'' is often used instead. Smart data includes, among other things, the clever selection of data for analysis and the combination of data-driven procedures and expert knowledge for their analysis.…”

Section: Interaction Of the Observed Chemical Information With Procesmentioning

confidence: 99%

Calibration‐Free Chemical Process and Quality Control Units as Enablers for Modular Production

Bornemann‐Pfeiffer

Kern

Maiwald

et al. 2020

Chemie Ingenieur Technik

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Dedicated to Prof. Dr.-Ing. Matthias Kraume on the occasion of his 65th birthday Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and, thus, a quicker time to market by flexibly connecting and orchestrating standardized physical modules and bringing them to life (i.e., parameterizing them) with digitally accumulated process knowledge. We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes.

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Community Detection and Analysis on Attributed Social Networks

Atzmueller¹

2017

Encyclopedia of Social Network Analysis and Mining

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Big und Smart Data

Cited by 8 publications

References 10 publications

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Calibration‐Free Chemical Process and Quality Control Units as Enablers for Modular Production

Community Detection and Analysis on Attributed Social Networks

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