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Using Machine Learning Techniques for Evaluating the Similarity of Enterprise Architecture Models

Technical Paper
  • Vasil Borozanov
  • Simon HacksEmail author
  • Nuno Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

Enterprises Architectures (EA) are facilitated to coordinate enterprise’s business visions and strategies successfully and effectively. The practitioners of EA (architects) communicate the architecture to other stakeholders via architecture models. We investigate the scenario where accepted architecture models are stored in a repository. We identified the problem of unnecessary repository expansion by adding model components with similar properties or behavior as already existing repository components. The proposed solution aims to find those similar components and to notify the architect about their existence.

We present two approaches for defining and combining similarities between EA model components. The similarity measures are calculated upon the properties of the components and on the context of their usage. We further investigate the behavior of similar architecture models and search for associations in order to obtain components that might be of interest. At the end, we provide a prototype tool for both generating requests and obtaining a result.

Keywords

Enterprise architecture Model Graph Machine learning 

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)Google Scholar
  2. 2.
    Ahlemann, F., Stettiner, E., Messerschmidt, M., Legner, C.: Strategic Enterprise Architecture Management: Challenges, Best Practices, and Future Developments. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-24223-6CrossRefGoogle Scholar
  3. 3.
    Aier, S., Schoenherr, M.: Integrating an enterprise architecture using domain clustering. In: Lankhorst, M.M., Johnson, P. (eds.) Proceedings of the Second Workshop on Trends in Enterprise Architecture Research, pp. 23–30, June 2007Google Scholar
  4. 4.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks. In: ICWSM (2009)Google Scholar
  5. 5.
    Champin, P.-A., Solnon, C.: Measuring the similarity of labeled graphs. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 80–95. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-45006-8_9CrossRefGoogle Scholar
  6. 6.
    Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-00234-2CrossRefzbMATHGoogle Scholar
  7. 7.
    Dijkman, R., Dumas, M., Van Dongen, B., Käärik, R., Mendling, J.: Similarity of business process models: Metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011)CrossRefGoogle Scholar
  8. 8.
    Dreyfus, D., Iyer, B.: Enterprise architecture: a social network perspective. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS 2006), vol. 8 (2006)Google Scholar
  9. 9.
    Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  10. 10.
    Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM, New York (2002)Google Scholar
  11. 11.
    La Rosa, M., Dumas, M., Uba, R., Dijkman, R.: Business process model merging: an approach to business process consolidation. ACM Trans. Softw. Eng. Methodol. 22(2), 11:1–11:42 (2013)Google Scholar
  12. 12.
    Navarro, G.: A guided tour to approximate string matching. ACM Comput. Surv. 33(1), 31–88 (2001)CrossRefGoogle Scholar
  13. 13.
    Neto, J.L., et al.: Document clustering and text summarization (2000)Google Scholar
  14. 14.
    Peffers, K., Tuunanen, T., Rothenberger, M., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24(3), 45–77 (2007)CrossRefGoogle Scholar
  15. 15.
    Rood, M.A.: Enterprise architecture: definition, content, and utility. In: Proceedings of 3rd IEEE Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 106–111 (1994)Google Scholar
  16. 16.
    Santana, A., Souza, A., Simon, D., Fischbach, K., De Moura, H.: Network science applied to enterprise architecture analysis: towards the foundational concepts. In: 2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC), pp. 10–19. IEEE (2017)Google Scholar
  17. 17.
    Santini, S., Jain, R.: Similarity measures. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 871–883 (1999)CrossRefGoogle Scholar
  18. 18.
    Schoonjans, A.: Social network analysis techniques in enterprise architecture management. Ph.D. thesis, Ghent University (2016)Google Scholar
  19. 19.
    Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, Boston, MA (2011).  https://doi.org/10.1007/978-0-387-85820-3_8CrossRefGoogle Scholar
  20. 20.
    Sichi, J., Kinable, J., Michail, D., Naveh, B., Contributors: JGraphT - Graph Algorithms and Data Structures in Java (Version 1.1.0) (2017). http://www.jgrapht.org
  21. 21.
    Simon, D., Fischbach, K.: IT landscape management using network analysis. In: Poels, G. (ed.) CONFENIS 2012. LNBIP, vol. 139, pp. 18–34. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-36611-6_2CrossRefGoogle Scholar
  22. 22.
    Tamm, T., Seddon, P., Shanks, G., Reynolds, P.: How does enterprise architecture add value to organisations? Commun. Assoc. Inf. Syst. 28, 141–168 (2011)Google Scholar
  23. 23.
    The Open Group: TOGAF Version 9.1. Van Haren Publishing, Zaltbommel (2011)Google Scholar
  24. 24.
    van der Raadt, B., van Vliet, H.: Designing the enterprise architecture function. In: Becker, S., Plasil, F., Reussner, R. (eds.) QoSA 2008. LNCS, vol. 5281, pp. 103–118. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-87879-7_7CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Research Group Software ConstructionRWTH Aachen UniversityAachenGermany
  2. 2.Department of Computer Science and EngineeringTechnical University of LisbonLisbonPortugal

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