A Construction Approach of Model Transformation Rules Based on Rough Set Theory

  • Jin Li
  • Dechen Zhan
  • Lanshun Nie
  • Xiaofei Xu
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 76)


Model transformation rules are the central part of model transformation. Many model transformation approaches provide some mechanisms to construct transformation rules in industrial and academic research. However, transformation rules are typically created manually in these approaches. As far as we know, there are no complete solutions that construct transformation rules automatically. In this paper, we propose a rough set based approach to construct transformation rules semi-automatically. Construction approach of rough set is improved in order to support the transformations between different meta-models, then the corresponding algorithm to construct transformation rules is presented. We also provide the measurement indicators of transformation rules to support selecting proper rules from many rules which meet transformation requirement. Three kinds of experiments for problems with distinct complexity and size are given for the validation of the proposed method.


Model transformation Model transformation rules Rough set theory 


  1. 1.
    Mukerji J, Miller J.: MDA guide version1.0.1. OMG (2003),
  2. 2.
    Kleppe, A., Warmer, J., Bast, W.: MDA Explained: The Model Driven Architecture: Practice and Promise. Addison-Wesley, Boston (2003)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Quest Synthetic Data Generator,
  4. 4.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)Google Scholar
  5. 5.
    Varró, D.: Model transformation by example. In: Wang, J., et al. (eds.) MoDELS 2006. LNCS, vol. 4199, pp. 410–424. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Pawlak, Z., Skowron, A.: Rough sets rudiments. Bulletin of IRSS 3(3), 67–70 (1999)Google Scholar
  7. 7.
    Liyun, C., Guoyin, W., Yu, W.: An Approach for Attribute Reduction and Rule Generation Based on Rough Set Theory. Journal of Software 10(11), 1206–1211 (1999)Google Scholar
  8. 8.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24, 833–849 (2003)CrossRefGoogle Scholar
  9. 9.
    Øhrn, A.: Discernibility and rough sets in medicine: tools and applications, Trondheim, Norway (1999)Google Scholar
  10. 10.
    Pawlak, A., Slowinski, R.: Rough set approach to multi-attribute decision analysis. European Journal of Operational Research 72(3), 443–459 (1994)CrossRefGoogle Scholar
  11. 11.
    Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning. Information Sciences 177(1), 41–73 (2007)CrossRefGoogle Scholar
  12. 12.
    Shane, S.: Combining generative and graph transformation techniques for model transformation: An effective alliance? In: Proc. of the 2nd OOPSLA Workshop on Generative Techniques in the context of Model Driven Architecture. ACM Press, Anaheim (2003), Google Scholar
  13. 13.
    Karsai, G., Agrawal, A.: Graph transformations in oMG’s model-driven architecture. In: Pfaltz, J.L., Nagl, M., Böhlen, B. (eds.) AGTIVE 2003. LNCS, vol. 3062, pp. 243–259. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Dhamanka, R., Lee, Y., Doan, A., Halevy, A., Domingos, P.: iMAP: discovering complex semantic matches between database schemas. In: Proceedings of ACM SIGMOD 2004, pp. 383–394. ACM, New York (2004)CrossRefGoogle Scholar
  15. 15.
    David, K., Stuart, A.: A relational approach to defining transformations in a metamodel. In: Jézéquel, J.-M., Hussmann, H., Cook, S. (eds.) UML 2002. LNCS, vol. 2460, pp. 243–258. Springer, Heidelberg (2002)Google Scholar
  16. 16.
    Miller, R.J., Hernandez, M.A., Haas, L.M., Yan, L.-L., Ho, C.T.H., Fagin, R., Popa, L.: The Clio Project: Managing heterogeneity. SIGMOD Record 30(1), 78–83 (2001)CrossRefGoogle Scholar
  17. 17.
    Melnik, S.: Generic Model Management: Concepts and Algorithms. Ph.D. Dissertation. LNCS, vol. 2967. Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Tian, Z., Yan, Z., Xiaofeng, Y., et al.: MDA Based Design Patterns Modeling and Model Transformation. Journal of Software 19(9), 2203–2217 (2008)CrossRefGoogle Scholar
  19. 19.
    Del Fabro, M.D., Valduriez, P.: Towards the efficient development of model transformations using model weaving and matching transformations. Software and System Modeling 8(3), 305–324 (2009)CrossRefGoogle Scholar
  20. 20.
    Balogh, Z., Varró, D.: Model Transformation by Example Using Inductive Logic Programming. Software and System Modeling 8(3), 347–364 (2009)CrossRefGoogle Scholar
  21. 21.
    Wimmer, M., Strommer, M., Kargl, H., Kramler, G.: Towards model transformation generation by-example. In: Proceedings of HICSS-40 Hawaii International Conference on System Sciences, p. 285. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  22. 22.
    Panetto, H., Scannapieco, M., Zelm, M.: INTEROP noE: Interoperability research for networked enterprises applications and software. In: Chung, S., Corsaro, A. (eds.) OTM-WS 2004. LNCS, vol. 3292, pp. 866–882. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Bauer, B., Müller, J.P., Roser, S.: A Model-Driven Approach to Designing Cross-Enterprise Business Processes. In: Chung, S., Corsaro, A. (eds.) OTM-WS 2004. LNCS, vol. 3292, pp. 544–555. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Jin Li
    • 1
    • 2
  • Dechen Zhan
    • 1
  • Lanshun Nie
    • 1
  • Xiaofei Xu
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.School of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

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