Advertisement

Software & Systems Modeling

, Volume 13, Issue 2, pp 825–841 | Cite as

Automatic data collection for enterprise architecture models

  • Hannes Holm
  • Markus Buschle
  • Robert Lagerström
  • Mathias Ekstedt
Regular Paper

Abstract

Enterprise Architecture (EA) is an approach used to provide decision support based on organization-wide models. The creation of such models is, however, cumbersome as multiple aspects of an organization need to be considered, making manual efforts time-consuming, and error prone. Thus, the EA approach would be significantly more promising if the data used when creating the models could be collected automatically—a topic not yet properly addressed by either academia or industry. This paper proposes network scanning for automatic data collection and uses an existing software tool for generating EA models (ArchiMate is employed as an example) based on the IT infrastructure of enterprises. While some manual effort is required to make the models fully useful to many practical scenarios (e.g., to detail the actual services provided by IT components), empirical results show that the methodology is accurate and (in its default state) require little effort to carry out.

Keywords

Enterprise architecture Automatic data collection Network scanning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ross J.W., Weill P., Robertson D.: Enterprise Architecture As Strategy: Creating a Foundation for Business Execution. Harvard Business School Press, Boston (2006)Google Scholar
  2. 2.
    The Open Group: The Open Group Architecture Framework (TOGAF), version 9, The Open Group (2009)Google Scholar
  3. 3.
    Zachman J.A.: A framework for information systems architecture. IBM Syst. J. 26, 276–292 (1987)CrossRefGoogle Scholar
  4. 4.
    Lankhorst M.M.: Enterprise Architecture at Work: Modelling, Communication and Analysis, 2nd edn. Springer, Berlin (2009)CrossRefGoogle Scholar
  5. 5.
    Winter R., Fischer R.: Essential layers, artifacts, and dependencies of enterprise architecture. J. Enterp. Archit. 3, 7–18 (2007)Google Scholar
  6. 6.
    Kurpjuweit, S., Winter, R.: Viewpoint-based meta model engineering. In: Enterprise Modelling and Information Systems Architectures (EMISA 2007)Google Scholar
  7. 7.
    Aier, S., Buckl, S., Franke, U., Gleichauf, B., Johnson, P., Närman, P., Schweda, C., Ullberg, J.: A survival analysis of application life spans based on enterprise architecture models. In: 3rd International Workshop on Enterprise Modelling and Information Systems Architectures, Ulm, Germany, pp. 141–154 (2009)Google Scholar
  8. 8.
    BiZZdesign: BiZZdesign Architect. http://www.bizzdesign.com (2011). Accessed on March 2011
  9. 9.
    Troux Technologies: Metis. http://www.troux.com/products/ (2011). Accessed on March 2011
  10. 10.
    Sousa P., Lima J., Sampaio A., Pereira C.: An approach for creating and managing enterprise blueprints: a case for it blueprints, Advances in Enterprise Engineering III. Lecture Notes Bus. Inf. Process. 34, 70–84 (2009)CrossRefGoogle Scholar
  11. 11.
    Hafner, M., Winter, R.: Processes for enterprise application architecture management. In: Proceedings of the 41st Hawaii International Conference on System Sciences, pp. 396–406 (2008)Google Scholar
  12. 12.
    Breu, R.: Ten principles for living models—a manifesto of change-driven software engineering. In: International Conference on Complex, Intelligent and Software Intensive Systems, pp. 1–8 (2010)Google Scholar
  13. 13.
    Buckl, S., Matthes, F., Neubert, C., Schweda, C.M.: A wiki-based approach to enterprise architecture documentation and analysis. In: 17th European Conference on Information Systems, pp. 1–13 (2009)Google Scholar
  14. 14.
    Buschle, M., Holm, H., Sommestad, T., Ekstedt, M., Shahzad, K.: A tool for automatic enterprise architecture modeling. In: Proceedings of the CAiSE Forum 2011, pp. 25–32 (2011)Google Scholar
  15. 15.
    Buschle, M., Ullberg, J., Franke, U., Lagerström, R., Sommestad, T.: A tool for enterprise architecture analysis using the PRM formalism. In: CAiSE2010 Forum PostProceedings, pp. 108–121 (2010)Google Scholar
  16. 16.
    MooD International: MooD Business Architect. http://www.moodinternational.com/ (2012). Acessed on March 2012
  17. 17.
  18. 18.
    Lokomo Systems AB: OneCMDB. http://www.onecmdb.org (2011)
  19. 19.
    FrontRange Solutions: FrontRange CMDB. http://www.frontrange.com/cmdb.aspx (2011)
  20. 20.
    Aier S., Kurpjuweit S., Saat J., Winter R.: Enterprise architecture design as an engineering discipline. AIS Trans. Enterp. Syst. 1(1), 36–43 (2009)Google Scholar
  21. 21.
    Fischer R., Aier S., Winter R.: A federated approach to enterprise architecture model maintenance. Enterp. Modell. Inf. Syst. Archit. 2(2), 14–22 (2007)Google Scholar
  22. 22.
    Holm H., Sommestad T., Almroth J., Persson M.: A quantitative evaluation of vulnerability scanning. Inf. Manage. Comput. Secur. 19(4), 231–247 (2011)CrossRefGoogle Scholar
  23. 23.
    Holm H.: Performance of automated network vulnerability scanning at remediating security issues. Comput. Secur. 31(2), 164–175 (2012)CrossRefGoogle Scholar
  24. 24.
    Johnson, P., Ekstedt, M.: Enterprise Architecture—Models and Analyses for Information Systems Decision Making, Studentlitteratur (2007)Google Scholar
  25. 25.
    Lagerström R., Johnson P., Ekstedt M.: Architecture analysis of enterprise systems modifiability—a metamodel for software change cost estimation. Softw. Qual. J. 18, 437–468 (2010)CrossRefGoogle Scholar
  26. 26.
    Närman, P., Johnson, P., Nordström, L.: Enterprise architecture: a framework supporting system quality analysis. In: Proceedings of the International Annual Enterprise Distributed Object Computing Conference, pp. 130–142 (2007)Google Scholar
  27. 27.
    Ullberg, J., Lagerström, R., Johnson, P.: A framework for service interoperability analysis using enterprise architecture models. In: IEEE International Conference on Services Computing, pp. 99–107 (2008)Google Scholar
  28. 28.
    Franke, U., Flores, W.R., Johnson, P.: Enterprise architecture dependency analysis using fault trees and bayesian networks. In: Proceedings of 42nd Annual Simulation Symposium (ANSS), pp. 209–216 (2009). http://www.scs.org
  29. 29.
    Gustafsson, P., Höök, D., Ericsson, E., Lilliesköld, J.: Analyzing IT impacts on organizational structure—a case study, In: Portland International Center for Management of Engineering and Technology (PICMET) Conference Proceedings, pp. 3197–3210 (2009)Google Scholar
  30. 30.
    IEEE: 1471–2000—IEEE Recommended Practice for Architectural Description for Software-Intensive Systems (2000). http://standards.ieee.org
  31. 31.
    The Open Group: ArchiMate 1.0 Specification (2009). http://www.opengroup.org/archimate
  32. 32.
    Manzuik, S., Pfeil, K., Gold, A., Gatford, C.: Network security assessment: from vulnerability to patch, Syngress (2006)Google Scholar
  33. 33.
    Rapid7: Nexpose. http://www.rapid7.com (2011)
  34. 34.
    Network mapper: Nmap. http://nmap.org (2011)
  35. 35.
    Hammervik, M., Andersson, D., Hallberg, J.: Capturing a cyber defence exercise. In: Proceedings of the Symposium on Technology and Methodology for Security and Crisis Management, p. 36 (2010)Google Scholar
  36. 36.
    Geers K.: Live fire exercise: preparing for cyber war. J. Homeland Secur. Emerg. Manage. 7(1), 74 (2010)Google Scholar
  37. 37.
    Warner R.: Applied statistics: from bivariate through multivariate techniques. Sage Publications, Inc, Thousand Oaks (2008)Google Scholar
  38. 38.
    Närman P., Holm H., Johnson P., König J., Chenine M., Ekstedt M.: Data accuracy assessment using enterprise architecture. Enterp. Inf. Syst. 5(1), 37–58 (2011)CrossRefGoogle Scholar
  39. 39.
    Ye, K., Jiang, X., Chen, S., Huang, D., Wang, B.: Analyzing and modeling the performance in xen-based virtual cluster environment. In: 2010 12th IEEE International Conference on High Performance Computing and Communications, IEEE, pp. 273–280 (2010)Google Scholar
  40. 40.
    McDougall R., Anderson J.: Virtualization performance: perspectives and challenges ahead. ACM SIGOPS Oper. Syst. Rev. 44(4), 40–56 (2010)CrossRefGoogle Scholar
  41. 41.
    Wang, G., Ng, T.: The impact of virtualization on network performance of amazon ec2 data center. In: INFOCOM, 2010 Proceedings IEEE, IEEE, pp. 1–9 (2010)Google Scholar
  42. 42.
    Farwick, M., Agreiter, B., Breu, R., Ryll, S., Voges, K., Hanschke, T.: Automation processes for enterprise architecture management. In: 2011 15th IEEE International Enterprise Distributed Object Computing Conference Workshops, IEEE, pp. 340–349 (2011)Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Hannes Holm
    • 1
  • Markus Buschle
    • 1
  • Robert Lagerström
    • 1
  • Mathias Ekstedt
    • 1
  1. 1.Industrial Information and Control SystemsRoyal Institute of TechnologyStockholmSweden

Personalised recommendations