Matching Workflow Contexts for Collaborative New Product Development Task Knowledge Provisioning

  • Tingyu Liu
  • Huifen Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8683)


The variety of product types and spesifications make new product development (NPD) tasks tough work in discrete manufacturing enterprises, which makes it a common strategy to refer to similar outcomes (e.g. the product drawings, work instructions, etc.) of former tasks during NPD processes. To improve the efficiency of discovering similar historical outcomes, this paper presents a novel approach to measure the similarity between task execution contexts in process-aware information systems, and exploit it for runtime task knowledge recommendation. In our framework, the measurement of similarity is preceded by 1) modeling the task context with ontology theory, 2) using the ontology matching algorithms to evaluate the similarities between the corresponding context ontology entities of different tasks instances. The TD-IDF is then utilized to compute the context cohesion between the user’s current task and historical tasks, and the tasks with the highest similarity will be recommended to the task executors, along with their outcomes. Comparative evaluation is performed using TF-IDF, Levenshtein and Affine Gaps, and results demonstrate that the proposed approach achieves good precision and recall, and is efficient in task knowledge recommendation.


context-aware workflow collaborative knowledge management ontology matching TF-IDF 


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  1. 1.
    Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horváth, I., Bernard, A., Harik, R.F., Gao, W.: The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design 45, 204–228 (2013)CrossRefGoogle Scholar
  2. 2.
    Xu, L.: Enterprise systems: state-of-the-art and future trends. IEEE Transactions on Industrial Informatics 7, 630–640 (2011)CrossRefGoogle Scholar
  3. 3.
    Vanderfeesten, I., Reijers, H.A., Van der Aalst, W.M.: Product-based workflow support. Information Systems 36, 517–535 (2011)CrossRefGoogle Scholar
  4. 4.
    Harmon, P.: The scope and evolution of business process management. In: Handbook on Business Process Management, vol. 1, pp. 37–81. Springer (2010)Google Scholar
  5. 5.
    Alexopoulos, K., Makris, S., Xanthakis, V., Chryssolouris, G.: A web-services oriented workflow management system for integrated digital production engineering. CIRP Journal of Manufacturing Science and Technology 4, 290–295 (2011)CrossRefGoogle Scholar
  6. 6.
    Farhoomand, A.F., Drury, D.H.: Managerial information overload. Commun. ACM 45, 127–131 (2002)CrossRefGoogle Scholar
  7. 7.
    Zhen, L., Jiang, Z., Song, H.-T.: Distributed knowledge sharing for collaborative product development. International Journal of Production Research 49, 2959–2976 (2011)CrossRefGoogle Scholar
  8. 8.
    Li, B.M., Xie, S.Q., Xu, X.: Recent development of knowledge-based systems, methods and tools for One-of-a-Kind Production. Knowledge-Based Systems 24, 1108–1119 (2011)CrossRefGoogle Scholar
  9. 9.
    Liu, T., Cheng, Y., Ni, Z.: Mining Event Logs to Support Workflow Resource Allocation. Knowledge-Based Systems 35, 320–331 (2012)CrossRefGoogle Scholar
  10. 10.
    Abecker, A., Bernardi, A., Maus, H., Sintek, M., Wenzel, C.: Information supply for business processes: coupling workflow with document analysis and information retrieval. Knowledge-Based Systems 13, 271–284 (2000)CrossRefGoogle Scholar
  11. 11.
    Abecker, A.: Business-process oriented knowledge management: concepts, methods, and tools. Forschungszentrum Informatik, vol. Doctor. University of Karlsruhe (TH), Karlsruhe (2004)Google Scholar
  12. 12.
    Zhuge, H.: A process matching approach for flexible workflow process reuse. Information and Software Technology 44, 445–450 (2002)CrossRefGoogle Scholar
  13. 13.
    Zhuge, H.: Knowledge flow management for distributed team software development. Knowledge-Based Systems 15, 465–471 (2002)CrossRefGoogle Scholar
  14. 14.
    Zhuge, H.: A knowledge flow model for peer-to-peer team knowledge sharing and management. Expert Systems with Applications 23, 23–30 (2002)CrossRefGoogle Scholar
  15. 15.
    Zhen, L., Huang, G.Q., Jiang, Z.: Collaborative filtering based on workflow space. Expert Systems with Applications 36, 7873–7881 (2009)CrossRefGoogle Scholar
  16. 16.
    Zhen, L., Huang, G.Q., Jiang, Z.: Recommender system based on workflow. Decision Support Systems 48, 237–245 (2009)CrossRefGoogle Scholar
  17. 17.
    Liu, D.-R., Lai, C.-H., Chen, Y.-T.: Document recommendations based on knowledge flows: A hybrid of personalized and group-based approaches. Journal of the American Society for Information Science and Technology 63, 2100–2117 (2012)CrossRefGoogle Scholar
  18. 18.
    Harms, R., Fleschutz, T., Seliger, G.: Life cycle management of production facilities using semantic web technologies. CIRP Annals - Manufacturing Technology 59, 45–48 (2010)CrossRefGoogle Scholar
  19. 19.
    Christophe, F., Bernard, A., Coatanéa, É.: RFBS: A model for knowledge representation of conceptual design. CIRP Annals - Manufacturing Technology 59, 155–158 (2010)CrossRefGoogle Scholar
  20. 20.
    Strang, T., Linnhoff-Popien, C.: A Context Modeling Survey. In: Workshop on Advanced Context Modelling, Reasoning and Management, UbiComp 2004 - The Sixth International Conference on Ubiquitous Computing (2004)Google Scholar
  21. 21.
    Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing 6, 161–180 (2010)CrossRefGoogle Scholar
  22. 22.
    Bolchini, C., Curino, C.A., Quintarelli, E., Schreiber, F.A., Tanca, L.: A data-oriented survey of context models. ACM Sigmod Record 36, 19–26 (2007)CrossRefGoogle Scholar
  23. 23.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)CrossRefzbMATHGoogle Scholar
  24. 24.
    Porter, M.: An algorithm for suffix stripping. Program 14, 8 (1980)CrossRefGoogle Scholar
  25. 25.
    Christopher, D., Manning, P.R.: Hinrich Schütze: Introduction to Information Retrieval. Cambridge University Press, Cambridge, England (2009)Google Scholar
  26. 26.
    Liu, T., Ni, Z., Jiao, L., Liu, X.: The stem-based vector space model for automatic resource allocation in workflow. In: 2010 2nd International Conference on Computer Engineering and Technology (ICCET), pp. V1-631–634 (2010)Google Scholar
  27. 27.
    van der Aalst, W., van Hee, K.: Workflow Management: Models, Methods, and Systems. The MIT Press (2004)Google Scholar
  28. 28.
    Ristad, E.S., Yianilos, P.N.: Learning string-edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 522–532 (1998)CrossRefGoogle Scholar
  29. 29.
    Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: ACM SIGKDD, pp. 39–48 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tingyu Liu
    • 1
  • Huifen Wang
    • 1
  1. 1.School of Mechanical EngineeringNanjing University of Science and TechnologyNanjingP.R. China

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