Service Extraction from Operator Procedures in Process Industries

  • Jingwen He
  • Sandeep Purao
  • Jon Becker
  • David Strobhar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6629)

Abstract

Procedures are a common knowledge form in process industries such as refineries. A typical refinery captures hundreds of procedures documenting actions that operators must follow. Maintaining the action-knowledge contained in these procedures is important because it represents a key organizational asset that can be leveraged to minimize the threat of accidents. We develop an approach that extracts services from these operator procedures. The paper describes the heuristics underlying this approach, illustrates its application, and discusses implications.

Keywords

Service Extraction Knowledge Modules Knowledge Representation Heuristics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ham, D.-H., Yoon, W.C.: The Training Effects of Principle Knowledge on Fault Diagnosis Performance. Human Factors and Ergonomics in Manufacturing & Service Industries 17, 263–282 (2007)CrossRefGoogle Scholar
  2. 2.
    Jain, A., Akkisetty, V.P.K., Hailemariam, L.M., Suresh, P., Joglekar, G., Venkatasubramanian, V., Morris, K.R., Reklaitis, G.V.: An Ontological Framework for Knowledge Modeling in Pharmaceutical Product Development. In: The Preliminary Program for 2007 Annual Meeting, New Brunswick, NJ (2007)Google Scholar
  3. 3.
    Jia, X., Zhang, Z., Tian, X.: Research and Application on Typical Process Knowledge Discovery in Mechanical Manufacturing Enterprise. In: First International Workshop on Knowledge Discovery and Data Mining, WKDD 2008, pp. 196–200 (2008)Google Scholar
  4. 4.
    Madsen, E.S., Riis, J.O., Waehrens, B.V.: The Knowledge Dimension of Manufacturing Transfers: A Method for Identifying Hidden Knowledge. Strategic Outsourcing: An International Journal 1, 198–209 (2008)CrossRefGoogle Scholar
  5. 5.
    Ohashi, M., Yuki, Y.: Productivity Improvement by Automating Operators’ Knowledge and Experience. In: Proceedings of the 41st SICE Annual Conference, vol. 2, pp. 999–1003 (2002)Google Scholar
  6. 6.
    Backus, B.A.: Factors Related to The Economic Sustainability of Two Year Chemistry-Based Technology Training Programs Education. Oregon State University Corvallis (2009)Google Scholar
  7. 7.
    Bera, P., Wand, Y.: A Framework to Clarify the Role of Knowledge Management Systems. In: Pacific Asia Conference on Information Systems, Hyderabad, India, pp. 68–81 (2009)Google Scholar
  8. 8.
    Wolf, F.G.: Operationalizing and Testing Normal Accident Theory in Petrochemical Plants and Refineries. Production and Operations Management 10, 292–305 (2001)CrossRefGoogle Scholar
  9. 9.
    Grote, G.: Diagnosis of Safety Culture: A Replication and Extension towards Assessing “Safe” Organizational Change Processes. Safety Science 46, 450–460 (2008)CrossRefGoogle Scholar
  10. 10.
    Hsua, S.H., Lee, C.-C., Wu, M.-C., Takano, K.: A Cross-Cultural Study of Organizational Factors on Safety: Japanese vs. Taiwanese Oil Refinery Plants. Accident Analysis & Prevention 40, 24–34 (2008)CrossRefGoogle Scholar
  11. 11.
    Rowe, K.: Bottlenecks and Constraints within The Local Labour Market for Engineers in The Petrochemical Industry Sector: A Case Study of Engen Refinery, Wentworth. Industrial, Organisational and Labour Studies. University of KwaZulu-Natal, Durban (2009)Google Scholar
  12. 12.
    Nivolianitou, Z., Konstandinidou, M., Michalis, C.: Statistical Analysis of Major Accidents in Petrochemical Industry Notified to the Major Accident Reporting System (MARS). Journal of Hazardous Materials 137, 1–7 (2006)CrossRefGoogle Scholar
  13. 13.
    Strahan, A.: Oil Industry in U.S. Needs Engineers, Courts Retirees, Students (2005), http://www.bloomberg.com/apps/news?pid=newsarchive&sid=abwZ5OoPVmZE&refer=us#share
  14. 14.
    Jamieson, G.A.: Empirical Evaluation of an Industrial Application of Ecological Interface Design. Human Factors and Ergonomics Society Annual Meeting Proceedings 46, 536–540 (2002)CrossRefGoogle Scholar
  15. 15.
    Newell, A.: The Knowledge Level. Artificial Intelligence 18, 87–127 (1982)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Newell, A., Simon, H.A.: Computer Science as Empirical Inquiry: Symbols and Search. Communications of the ACM 19, 113–126 (1976)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Blosch, M.: Pragmatism and Organizational Knowledge Management. Knowledge and Process Management 8, 39–47 (2001)CrossRefGoogle Scholar
  18. 18.
    Nonaka, I.: A Dynamic Theory of Organizational Knowledge Creation. Organization Science 5, 14–37 (1994)CrossRefGoogle Scholar
  19. 19.
    Kannan, K., Srivastava, B.: Promoting Reuse via Extraction of Domain Concepts and Service Abstractions from Design Diagrams. In: IEEE International Conference on Services Computing, Hawaii, vol. 1, pp. 265–272 (2008)Google Scholar
  20. 20.
    Matos, C.: Service Extraction from Legacy Systems. In: Ehrig, H., Heckel, R., Rozenberg, G., Taentzer, G. (eds.) ICGT 2008. LNCS, vol. 5214, pp. 505–507. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Jansen, S.: ServiciFi: Service Extraction from Decomposed Software Monoliths in The Financial Domain (2010), http://servicifi.files.wordpress.com/2010/06/serviceextraction.pdf
  22. 22.
    Sirin, E., Parsia, B., Hendler, J.: Filtering and Selecting Semantic Web Services with Interactive Composition Techniques. IEEE Intelligent Systems 19, 42–49 (2004)CrossRefGoogle Scholar
  23. 23.
    Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading (1984)Google Scholar
  24. 24.
    Newman, A.J., Pancheva, R., Ozawa, K., Neville, H.J., Ullman, M.T.: An Event-Related fMRI Study of Syntactic and Semantic Violations. Journal of Psycholinguistic Research 30, 339–364 (2001)CrossRefGoogle Scholar
  25. 25.
    Charniak, E.: Statistical Techniques for Natural Language Parsing. AI Magazine 18, 33–43 (1997)Google Scholar
  26. 26.
    Fellbaum, C.: WordNet an Electronic Lexical Database. MIT Press, Cambridge (1999)MATHGoogle Scholar
  27. 27.
    Dittrich, K., Dayal, U., Buchmann, A., McCarthy, D.: Rules Are Objects Too: A Knowledge Model for an Active, Object-Oriented Database System. In: Dittrich, K.R. (ed.) OODBS 1988. LNCS, vol. 334, pp. 129–143. Springer, Heidelberg (1988)CrossRefGoogle Scholar
  28. 28.
    Scriven, M.: Types of Evaluation and Types of Evaluator. American Journal of Evaluation 17, 151–161 (1996)CrossRefGoogle Scholar
  29. 29.
    Cleven, A., Gubler, P., Kai, M.H.: Design Alternatives for the Evaluation of Design Science Research Artifacts. In: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology, Philadelphia, Pennsylvania (2009)Google Scholar
  30. 30.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design Science in Information Systems Research. MIS Quarterly 28, 75–105 (2004)Google Scholar
  31. 31.
    Vaishnavi, V.K., Buchanan, G.C., Nevins, A.J.: Smart Objects: A Tool for Building Intelligent Support Systems. In: Proceeding of the Twenty-Sixth Hawaii International Conference on System Sciences, Wailea, HI, vol. 3, pp. 93–102 (1993)Google Scholar
  32. 32.
    Lee, J.-S., Hsu, P.-L.: Remote Supervisory Control of the Human-in-The-Loop System by Using Petri Nets and Java. IEEE Transactions on Industrial Electronics 50, 431–439 (2003)CrossRefGoogle Scholar
  33. 33.
    Cranor, L.F.: A Framework for Reasoning about the Human in The Loop. In: Proceedings of the 1st Conference on Usability, Psychology, and Security, San Francisco, California (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jingwen He
    • 1
  • Sandeep Purao
    • 1
  • Jon Becker
    • 1
  • David Strobhar
    • 2
  1. 1.College of Information Sciences and TechnologyThe Pennsylvania State UniversityUSA
  2. 2.Beville Engineering, Inc.USA

Personalised recommendations