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Data Processing in Industrie 4.0

Data Analysis and Knowledge Management in Industrie 4.0

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Abstract

The pressure on companies to increase their flexibility and efficiency in manufacturing is constantly increasing. Factory managers therefore need to be able to obtain information in real-time across physical production systems for better decision making. Transparency on a production- and strategic level, for example, offers the advantage of being able to respond more quickly to volatile demand (time-to-market) and helps in reducing lead- and down-times. This can lead to a significant production gain and competitive advantage. Current approaches are challenged to bring results from the IoT world to decision makers in an appropriate manner. We introduce data models that serve as a mediator to create a better understanding between factory owners and data analysts. Particular challenges lie in the orchestration of the complex process steps, the vertical transparency of information, as well as in mutually contradictory optimization calculi (e.g., cost, speed, quality, sustainability). Due to better communication between factory managers, data analysts and people working at the line-side, the previously mentioned configurations can be implemented more transparently and consequently more efficiently.

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Notes

  1. http://www.disrupt-project.eu/.

  2. http://www.boc-group.com.

  3. http://www.boc-group.com.

  4. https://www.adoxx.org/live/home.

  5. https://www.adoxx.org/live/olive.

  6. http://austria.omilab.org/psm/content/bdds/info.

  7. http://www.orbeet.eu.

References

  1. Löffler C, Westkämper E, Unger K (2011) Change drivers and adaptation of automotive manufacturing. International Conference on Manufacturing Systems (ICMS), p 6

    Google Scholar 

  2. Westkämper E, Zahn E, Balve P, Tilebein M (2000) Ansätze zur Wandlungsfähigkeit von Produktionsunternehmen, WT. Werkstattstechnik 90:22–26

    Google Scholar 

  3. Eirinakis P, Buenabad-Chavez J, Fornasiero R, Gokmen H, Mascolo J, Mourtos I, Spieckermann S, Tountopoulos V, Werner F, Woitsch R (2017) A proposal of decentralised architecture for optimised operations in manufacturing ecosystem collaboration. Working Conference on Virtual Enterprises PRO-VE

    Book  Google Scholar 

  4. Marz N, Warren J (2015) Big Data: Principles and best practices of scalable real-time data systems, Manning

    Google Scholar 

  5. Software AG (2016) Company white paper: the APAMA platform, under-the-covers: an in-depth view of Apama

    Google Scholar 

  6. Software AG (2016) Product fact sheet: universal messaging. https://www.softwareag.com/corporate/images/SAG_UniversalMessaging_FS_Sept13_v3.5_WEB_tcm16-111010.pdf. Accessed 13.02.2018

    Google Scholar 

  7. Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2007) KNIME: the Konstanz information miner, studies in classification, data analysis, and knowledge organization. Springer, Berlin, Heidelberg

    Google Scholar 

  8. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1):10–18

    Article  Google Scholar 

  9. R Core Team (2013) R: A language and environment for statistical, computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  10. Krumeich J, Zapp M, Mayer D, Werth D, Loos P (2016) Modeling complex event patterns in EPC-models and transforming them into an executable event pattern language. Multikonferenz Wirtschaftsinformatik (MKWI), pp 81–92

    Google Scholar 

  11. Krumeich J, Mehdiyev N, Werth D, Loos P (2015) Towards an extended metamodel of event-driven process chains to model complex event patterns. 2nd International Workshop on Event Modeling and Processing in Business Process Management. Springer, Cham, Switzerland

    Book  Google Scholar 

  12. Software AG (2017) Company white paper: why you need zementis, predictive analytics. https://resources.softwareag.com/products-analytics-decisions/why-zementis-whitepaper. Accessed 13.02.2018

    Google Scholar 

  13. Frost & Sullivan (2016) Automotive Industry IT Spending, CIO Focus, Trends, and Highest Growth Areas, Report

    Google Scholar 

  14. Woitsch R, Hrgovcic V (2011) Modelling knowledge: an open model approach. Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies

    Google Scholar 

  15. Guschlbauer E, Lichka C (2013) Umsetzung des Prozesscontrollings, Prozessmanagement für Experten, Impulse für aktuelle und wiederkehrende Themen. Springer Gabler, Berlin Heidelberg

    Google Scholar 

  16. Woitsch R., Process-Oriented Knowledge Management: A Service-based Approach,PhD Thesis, Vienna (2004)

  17. Woitsch R, Utz W, Hrgovcic V (2013) Integration von Prozess- und Wissensmanagement, Prozessmanagement für Experten, Impulse für aktuelle und wiederkehrende Themen. Springer Gabler, Berlin Heidelberg

    Google Scholar 

  18. Lichka C., Der modellbasierte Business Scorecarding-Ansatz zur Strategieoperationalisierung, University of Vienna, PhD Thesis (2006)

  19. Karagiannis D, Woitsch R (2010) Knowledge engineering in business process management, business process management 2, strategic alignment, governance, people and culture. Springer, Berlin Heidelberg

    Google Scholar 

  20. Roussopoulos N, Utz W (2016) Design semantics on accessibility in unstructured data environment, domain specific conceptual modelling, concepts, methods and tools. Springer, Berlin Heidelberg

    Google Scholar 

  21. Utz W, Woitsch R (2017) A model-based environment for data services: energy-aware behavioral triggering using ADOxx. Collaboration in a data-rich world. PRO-VE 2017. vol 506. Springer, Berlin Heidelberg

    Google Scholar 

  22. Karagiannis D, Mayr H, Mylopoulos J (2016) Domain specific conceptual modelling, concepts, methods and tools. Springer, Cham

    Book  Google Scholar 

  23. Wooldridge M (2002) An introduction to multi-agent systems. Wiley & Sons, Hoboken

    Google Scholar 

  24. Leitão P (2009) Agent-based distributed manufacturing control: a state-of-the-art survey. Eng Appl Artif Intell 22:979–991

    Article  Google Scholar 

  25. Middelhoek S, Hoogerwerf AC (1985) Smart sensors: when and where ? Sens Actuators 8(1):39–48

    Article  Google Scholar 

  26. Montironi MA, Castellini P, Stroppa L, Paone N (2014) Adaptive autonomous positioning of a robot vision system: application to quality control on production lines. Robot Comput Integr Manuf 30:489–498

    Article  Google Scholar 

  27. J‑ Parikh AD, Weihl WE (2004) Edge computing, extending enterprise applications to the edge of the internet. ACM New York

    Google Scholar 

  28. Satyanarayanan M, Simoens P, Xiao Y, Pillai P, Chen Z, Ha K, Hu W, Amos B (2015) Edge analytics in the Internet of things. IEEE Pervasive Comput 14:24–31

    Article  Google Scholar 

  29. Lee EA, Rabaey J, Hartmann B, Kubiatowicz J, Pister K, Sangiovanni-Vincentelli A, Seshia SA, Wawrzynek J, Wessel D, Jafari R, Jones D, Kumar V, Mangharam R, Pappas GJ, Rosing TS (2014) The swarm at the edge of the cloud. IEEE Des Test 31(3):8–20

    Article  Google Scholar 

  30. Kabáč M, Consel C, Volanschi N (2017) Designing parallel data processing for enabling large-scale sensor applications, Personal and Ubiquitous Computing

    Google Scholar 

  31. Rossiter J (2003) Model-based predictive control: a practical approach. CRC Press, Boca Raton

    Google Scholar 

  32. Bemporad A (2006) Model predictive control design: new trends and tools. Proceedings of 45th IEEE Conference on Decision and Control

    Google Scholar 

  33. Kouvaritakis B, Cannon M (2001) Non-linear predictive control: theory and practice, ISBN 978-0852969847, The Institution of Engineering and Technology, IEE Publishing

  34. Park K, Zheng R, Liu X (2012) Cyber-physical systems: milestones and research challenges. Int J Comput Telecommun Ind 36:1–7

    Google Scholar 

  35. Stojanovic N, Dinic M, Stojanovic L (2015) Big data process analytics for continuous process improvement in manufacturing. Big Data, IEEE Publishing, Santa Clara, CA, USA, pp 1398–1407

    Google Scholar 

  36. Heemels W, De Schutter B, Bemporad A (2001) Equivalence of hybrid dynamical models. Automatica 37:1085–1091

    Article  MATH  Google Scholar 

  37. Juloski A, Wieland S, Heemels WPMH (2005) A Bayesian approach to identification of hybrid systems. IEEE Trans Automat Contr 50(10):1520–1533

    Article  MathSciNet  MATH  Google Scholar 

  38. Ferrari-Trecate G, Muselli M, Liberati D, Morari M (2003) A clustering technique for the identification of piecewise affine systems. Automatica 39:205–217

    Article  MathSciNet  MATH  Google Scholar 

  39. Woitsch Robert, Hrgovcic Vedran, Robert B (2012) Knowledge product modelling for industry: the PROMOTE approach. 14th IFAC Symposium on Information Control Problems in Manufacturing, International Federation of Automatic Control

    Google Scholar 

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Author information

Authors and Affiliations

  1. Software AG, Altenkesseler Str. 17, 66115, Saarbrücken, Germany

    Frank Werner

  2. BOC Asset Management GmbH, Operngasse 20b, 1040, Vienna, Austria

    Robert Woitsch

Authors
  1. Frank Werner
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  2. Robert Woitsch
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Corresponding author

Correspondence to Frank Werner.

Additional information

The work on this paper is funded mainly by the European Commission through the DISRUPT project (H2020 FOF-11-2016, RIA project n. 723541, 2016-2019). The authors would also like to thank the contributions of the different partners of the DISRUPT project.

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Werner, F., Woitsch, R. Data Processing in Industrie 4.0. Datenbank Spektrum 18, 15–25 (2018). https://doi.org/10.1007/s13222-018-0277-x

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  • DOI: https://doi.org/10.1007/s13222-018-0277-x

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