Knowledge-Based Support for Complex Systems Exploration in Distributed Problem Solving Environments

  • Pavel A. Smirnov
  • Sergey V. Kovalchuk
  • Alexander V. Boukhanovsky
Part of the Communications in Computer and Information Science book series (CCIS, volume 394)


The work is aimed to the development of approaches to intelligent support of knowledge usage and generation process performed within simulation-based research. As contemporary e-Science tasks often require acquisition, integration and usage of complex knowledge belonging to different domains, the concept and technology for semantic integration and processing of knowledge used within complex systems simulation tasks were developed. Within proposed approach three main classes of knowledge considered are considered: domain-specific, IT, and general system-level knowledge. All these classes are needed to be integrated and coordinated to support the simulation process. Ontology-based technology is described as a core technique for unified multi-domain knowledge formalization and automatic or semi-automatic interconnection. Virtual Simulation Objects (VSO) concept and technology are described as a basic approach for development of domain-specific solutions to support of the whole simulation-based research process including model development, simulation running and results presentation.


problem solving environment e-science complex system simulation knowledge base ontology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hey, T., Tansley, S., Tolle, K.: The Fourth Paradigm. Data-Intensive Scientific, Discovery, Microsoft, 252 (2009)Google Scholar
  2. 2.
    Rice, J.R., Boisvert, R.F.: From Scientific Software Libraries to Problem-Solving Environments. IEEE Computational Science & Engineering 3(3), 44–53 (1996)CrossRefGoogle Scholar
  3. 3.
    Lublinsky, B.: Defining SOA as an architectural style (January 9, 2007),
  4. 4.
    Gil, Y., et al.: Examining the Challenges of Scientific Workflows. IEEE Computer 40(12), 24–32 (2007)CrossRefGoogle Scholar
  5. 5.
    Yu, J., Buyya, R.: A Taxonomy of Workflow Management Systems for Grid Computing. Journal of Grid Computing 3(3-4), 171–200 (2005)CrossRefGoogle Scholar
  6. 6.
    Boukhanovsky, A.V., Kovalchuk, S.V., Maryin, S.V.: Intelligent Software Platform for Complex System Computer Simulation: Conception, Architecture and Implementation, Izvestiya VUZov. Priborostroenie 10, 5–24 (2009) (in Russian)Google Scholar
  7. 7.
    Chandrasekaran, B., Josephson, J.R., Benjamins, V.R.: What Are Ontologies, and Why Do We Need Them? IEEE Intelligent Systems 14(1), 20–26 (1999)CrossRefGoogle Scholar
  8. 8.
    Chen, L., et al.: Semantics-assisted Problem Solving on the Semantic Grid. Journal of Computational Intelligence 21(2), 157–176 (2005)CrossRefGoogle Scholar
  9. 9.
    Silver, G.A., Lacy, L.W., Miller, J.A.: Ontology Based Representations of Simulation Models Following the Process Interaction World View. In: Winter Simulation Conference, pp. 1168–1176 (2006)Google Scholar
  10. 10.
    Hu, J., Zhang, H.: Ontology Based Collaborative Simulation Framework Using HLA and Web Services. Computer Science and Information Engineering 5, 702–706 (2009)Google Scholar
  11. 11.
    McPhillips, T., Bowers, S., Zinn, D., Ludäscher, B.: Scientific workflow design for mere mortals. Future Generation Computer Systems 25(5), 541–551 (2009)CrossRefGoogle Scholar
  12. 12.
    Shneiderman, B.: Science 2.0. Science 319, 1349–1350 (2008)CrossRefGoogle Scholar
  13. 13.
    Belloum, A., et al.: Collaborative e-Science Experiments and Scientific Workflow. IEEE Internet Computing 15(4), 39–47 (2011)CrossRefGoogle Scholar
  14. 14.
    Altintas, I., et al.: A Data Model for Analyzing User Collaborations in Workflow-Driven eScience. International Journal of Computers and Their Applications (IJCA), Special Issue on Scientific Workflows, Provenance and Their Applications 18(3), 160–180 (2011)MathSciNetGoogle Scholar
  15. 15.
    Konong, R., et al.: Using ontologies for resource description in the CineGrid Exchange. Future Generation Computer Systems 27(7), 960–965 (2011)CrossRefGoogle Scholar
  16. 16.
    Vidal, A.C.T., et al.: Defining and exploring a grid system ontology. In: International Workshop on Middleware for Grid Computing, Melbourne, Australia, vol. 16 (2006)Google Scholar
  17. 17.
    Foster, I., Kesselman, C.: Scaling System-Level Science: Scientific Exploration and IT Implications. IEEE Computer 39(11), 31–39 (2006)CrossRefGoogle Scholar
  18. 18.
    Mitrofanova, O.V., Konstantinova, N.S.: Ontologies as Knowledge Storing Systems, 54 (2008) (in Russian),
  19. 19.
    Gavrilova, T.A., Malinovskaya, O.L.: Multilevel knowledge structuring and flexible conceptual atlases design, Uchenye zapiski Kazanskogo universiteta. Fiziko-matematicheskie Nauki 153(4), 189–202 (2011) (in Russian)Google Scholar
  20. 20.
    Agarwal, S., Petrie, C.: An Alternative to the Top-Down Semantic Web of Services. IEEE Internet Computing 16(5), 94–97 (2012)CrossRefGoogle Scholar
  21. 21.
    Kovalchuk, S.V., et al.: Virtual Simulation Objects Concept as a Framework for System-Level Simulation. In: IEEE 8th International Conference on E-Science, pp. 1–8 (2012)Google Scholar
  22. 22.
    Kovalchuk, S., Larchenko, A., Boukhanovsky, A.: Knowledge-Based Resource Management for Distributed Problem Solving. In: Wang, Y., Li, T. (eds.) Knowledge Engineering and Management. AISC, vol. 123, pp. 121–128. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Suggested Upper Merged Ontology (SUMO),
  24. 24.
    Knyazkov, K.V., et al.: CLAVIRE: e-Science infrastructure for data-driven computing. Journal of Computational Science 3(6), 504–510 (2012)CrossRefGoogle Scholar
  25. 25.
    The official SWAN homepage,
  26. 26.
    Bezgodov, A.A., Boukhanovsky, A.V.: Virtual testbed for exploration of extreme dynamics of marine objects in irregular sea, Izvestiya VUZov. Priborostroenie 5, 98–100 (2011) (in Russian)Google Scholar
  27. 27.
    Vasilev, V.N., et al.: CLAVIRE: cloud computing platform for data-driven computing. Informacionno-izmeritelnye i Upravlayushie Sistemy 10(11), 7–16 (2012) (in Russian) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pavel A. Smirnov
    • 1
  • Sergey V. Kovalchuk
    • 1
  • Alexander V. Boukhanovsky
    • 1
  1. 1.Saint-Petersburg National University of Information Technologies, Mechanics and OpticsSaint-PetersburgRussia

Personalised recommendations