Advertisement

Big Data Challenges in Industrial Automation

  • Marek Obitko
  • Václav Jirkovský
  • Jan Bezdíček
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8062)

Abstract

Within the industrial domain including manufacturing a lot of various data is produced. For exploiting the data for lower level control as well as for the upper levels such as MES systems or virtual enterprises, the traditional business intelligence methods are becoming insufficient. At the same time, especially within internet companies, the Big Data paradigm is getting higher popularity due to the possibility of handling variety of large volume of quickly generated data, including their analysis and immediate actions. We discuss Big Data challenges in industrial automation domain, including describing and reviewing relevant applications and features. We pay special attention to the use of semantics and multi-agent systems. We also describe possible next steps for Big Data adoption within industrial automation and manufacturing.

Keywords

Big Data large scale data industrial automation manufacturing transportation smart grid internet of things plant data semantics agents 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balaji, P.G., Srinivasan, D.: Multi-Agent System in Urban Traffic Signal Control. IEEE Computational Intelligence Magazine 5(4), 43–51 (2010)Google Scholar
  2. 2.
    Becker, A., Sénéclauye, G., Purswani, P., Karekar, S.: Internet of Things. Atos White Paper (2012)Google Scholar
  3. 3.
    Bizer, C., Boncz, P., Brodie, M.L., Erling, O.: The Meaningful Use of Big Data: Four Perspectives – Four Challenges. SIGMOD Records 40(4) (December 2011)CrossRefGoogle Scholar
  4. 4.
    Carme, J., Jimenez, F.J.R.: Open Source Solutions for Big Data Management. Atos White Paper (2011)Google Scholar
  5. 5.
    Community white paper: Challenges and Opportunities with Big Data (2012)Google Scholar
  6. 6.
    Chui, M., Löffler, M., Roberts, R.: The Internet of Things. McKinsey Quarterly (2010)Google Scholar
  7. 7.
    Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  8. 8.
    Detica: Unblocking the transport network. Whitepaper (2010)Google Scholar
  9. 9.
    GE Intelligent Platforms: The Rise of Industrial Big Data. Whitepaper (2012)Google Scholar
  10. 10.
    IBM Software: Managing Big Data for smart grids and smart meters. Whitepaper (2012) Google Scholar
  11. 11.
    Lin, J., Sedigh, S., Hurson, A.R.: An Agent-Based Approach to Reconciling Data Heterogeneity in Cyber-Physical Systems. In: IEEE International Symposium on Parallel and Distributed Processing, pp. 93–103 (2011)Google Scholar
  12. 12.
    Kaisler, S., Armour, F., Espinosa, J.A., Money, W.: Big Data: Issues and Challenges Moving Forward. In: 46th Hawaii International Conference on System Sciences. IEEE Press (2013)Google Scholar
  13. 13.
    Manola, F., Miller, E. (eds.): RDF Primer. W3C Recommendation (2004)Google Scholar
  14. 14.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C.: Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute Report (2011)Google Scholar
  15. 15.
    NewVantage Partners: Big Data Executive Survey 2012. Consolidated Summary Report (2012)Google Scholar
  16. 16.
    North, M.J., Collier, N.T., Ozik, J., Tatara, E., Altaweel, M., Macal, C.M., Bragen, M., Sydelko, P.: Complex Adaptive Systems Modeling with Repast Simphony. In: Complex Adaptive Systems Modeling. Springer (2013)Google Scholar
  17. 17.
    Schlieski, T., Johnson, B.D.: Entertainment in the Age of Big Data. Proceedings of the IEEE 100, 1404–1408 (2012)CrossRefGoogle Scholar
  18. 18.
    Singh, S., Singh, N.: Big Data analytics. In: 2012 International Conference on Communication, Information & Computing Technology (ICCICT), Mumbai, India. IEEE Press (2012)Google Scholar
  19. 19.
    W3C OWL Working Group. OWL 2 Web Ontology Language Document Overview, 2nd edn. W3C Recommendation (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marek Obitko
    • 1
  • Václav Jirkovský
    • 1
    • 2
  • Jan Bezdíček
    • 1
  1. 1.Rockwell Automation Research CenterPragueCzech Republic
  2. 2.Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical UniversityPragueCzech Republic

Personalised recommendations