Enterprise Architecture Analytics and Decision Support

  • Rainer SchmidtEmail author
  • Michael Möhring
Part of the Intelligent Systems Reference Library book series (ISRL, volume 111)


The discipline of Enterprise Architecture Management started using a model-driven approach. In contrary to the model-driven approaches, our approach follows strives to tap also the information contained in the operational systems that support IT-Service-Management. Therefore, this paper aims at indicating the increased capabilities of Enterprise Architecture Analytics and Decision Support through the use of a data-driven approach. It will give fundamental insights in the current research work of enterprise architecture management analytics as well as decision support based on this quantitative data.


Enterprise architecture management IT-Service-Management Decision support Analytics 


  1. 1.
    Jonkers, H., Lankhorst, M.M., ter Doest, H.W., Arbab, F., Bosma, H., Wieringa, R.J.: Enterprise architecture: management tool and blueprint for the organisation. Inf. Syst. Frontiers. 8, 63–66 (2006)CrossRefGoogle Scholar
  2. 2.
    Schmidt, R., Möhring, M., Härting, R.-C., Reichstein, C., Zimmermann, A., Luceri, S.: Benefits of enterprise architecture management—insights from European experts. Presented at the PoEM 2015: 8th IFIP WG 8.1 working conference on the Practice of Enterprise Modelling, Berlin (2015)Google Scholar
  3. 3.
    Buckl, S., Ernst, A.M., Lankes, J., Matthes, F., Schweda, C.M.: Enterprise architecture management patterns–exemplifying the approach. In: Enterprise Distributed Object Computing Conference, 2008. EDOC’08. 12th International IEEE. pp. 393–402. IEEE (2008)Google Scholar
  4. 4.
    Aier, S., Riege, C., Winter, R.: Unternehmensarchitektur-Literaturüberblick und Stand der Praxis. Wirtschaftsinformatik 50, 292–304 (2008)CrossRefGoogle Scholar
  5. 5.
    Aier, S., Gleichauf, B., Winter, R.: Understanding enterprise architecture management design-an empirical analysis. In: Wirtschaftsinformatik, p. 50 (2011)Google Scholar
  6. 6.
    Power, D.J., Sharda, R., Burstein, F.: Decision support systems. Wiley Online Library (2002)Google Scholar
  7. 7.
    Lankhorst, M.M., Proper, H.A., Jonkers, H.: The architecture of the ArchiMate language. In: Enterprise, Business-Process and Information Systems Modeling, pp. 367–380 (2009)Google Scholar
  8. 8.
    Brenner, M., Garschhammer, M., Sailer, M., Schaaf, T.: CMDB-yet another MIB? On Reusing Management Model Concepts in ITIL Configuration Management. Large Scale Management of Distributed Systems, pp. 269–280 (2006)Google Scholar
  9. 9.
    ter Doest, H., Lankhorst, M.: Tool Support for Enterprise Architecture-A Vision. Telematica Instituut, Enschede (2004)Google Scholar
  10. 10.
    Buckl, S., Matthes, F., Schweda, C.M.: Future Research Topics in Enterprise Architecture Management—A Knowledge Management Perspective. In: Dan, A., Gittler, F., Toumani, F. (eds.) Service-Oriented Computing. ICSOC/ServiceWave 2009 Workshops. pp. 1–11. Springer Berlin Heidelberg (2010)Google Scholar
  11. 11.
    Roth, S., Matthes, F.: Future research topics in enterprise architectures evolution analysis. In: Software Engineering (Workshops), pp. 201–206 (2013)Google Scholar
  12. 12.
    Erol, S., Granitzer, M., Happ, S., Jantunen, S., Jennings, B., Johannesson, P., Koschmider, A., Nurcan, S., Rossi, D., Schmidt, R.: Combining BPM and social software: contradiction or chance? J. Softw. Maintenance Evol. Res. Pract. 22, 449–476 (2010)CrossRefGoogle Scholar
  13. 13.
    Farwick, M., Agreiter, B., Breu, R., Ryll, S., Voges, K., Hanschke, I.: Automation processes for enterprise architecture management. In: Enterprise Distributed Object Computing Conference Workshops (EDOCW), 2011 15th IEEE International, pp. 340–349. IEEE (2011)Google Scholar
  14. 14.
    Farwick, M., Schweda, C.M., Breu, R., Hanschke, I.: A situational method for semi-automated enterprise architecture documentation. Softw. Syst. Model. 1–30 (2014)Google Scholar
  15. 15.
    Correia, A., Abreu, F.: Integrating it service management within the enterprise architecture. In: Fourth International Conference on Software Engineering Advances, 2009. ICSEA’09, pp. 553–558 (2009)Google Scholar
  16. 16.
    Kimball, R., Ross, M., et al.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modelling. Wiley, New York [ua] (2002) (Nachdr)Google Scholar
  17. 17.
    Veneberg, R.K.M., Iacob, M.E., Van Sinderen, M.J., Bodenstaff, L.: Enterprise architecture intelligence: combining enterprise architecture and operational data. In: Enterprise Distributed Object Computing Conference (EDOC), 2014 IEEE 18th International, pp. 22–31 (2014)Google Scholar
  18. 18.
    Buschle, M., Ekstedt, M., Grunow, S., Hauder, M., Matthes, F., Roth, S.: Automating enterprise architecture documentation using an enterprise service bus (2012)Google Scholar
  19. 19.
    Johnson, P., Ekstedt, M.: Enterprise architecture: models and analyses for information systems decision making (2007)Google Scholar
  20. 20.
    Galup, S.D., Dattero, R., Quan, J.J., Conger, S.: An overview of IT service management. Commun. ACM 52, 124–127 (2009)CrossRefGoogle Scholar
  21. 21.
    Farwick, M., Breu, R., Hauder, M., Roth, S., Matthes, F.: Enterprise architecture documentation: Empirical analysis of information sources for automation. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 3868–3877. IEEE (2013)Google Scholar
  22. 22.
    Bär, F., Schmidt, R., Möhring, M.: Fabric-Process Patterns. In: Bider, I., Gaaloul, K., Krogstie, J., Nurcan, S., Proper, H.A., Schmidt, R., Soffer, P. (eds.) Enterprise, Business-Process and Information Systems Modeling, pp. 139–153. Springer, Berlin (2014)Google Scholar
  23. 23.
    Schmidt, R.: A framework for comparing cloud-environments. In: 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 553–556. IEEE, Stettin (2011)Google Scholar
  24. 24.
    List of Log Files in Configuration Manager: (2007)
  25. 25.
    Fensterer, M.: Supporting capacity planning of cloud computing data centers with long term trend analysis of performance monitoring data (2012)Google Scholar
  26. 26.
    Zikopoulos, P., Eaton, C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media (2011)Google Scholar
  27. 27.
    White, T.: Hadoop: The definitive guide. O’Reilly Media (2012)Google Scholar
  28. 28.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, pp. 10–10 (2010)Google Scholar
  29. 29.
    Schmidt, R., sotzki, M.W., Jugel, D., Möhring, M., Sandkuhl, K., Zimmermann, A.: Towards a framework for enterprise architecture analytics. In: Grossmann, G., Hallé, S., Karastoyanova, D., Reichert, M., Rinderle-Ma, S. (eds.) 18th IEEE International Enterprise Distributed Object Computing Conference Workshops and Demonstrations, EDOC Workshops 2014, Ulm, Germany, 1–2 Sep 2014, pp. 266–275. IEEE Computer Society (2014)Google Scholar
  30. 30.
    Schmidt, R., Zimmermann, A., Möhring, M., Jugel, D., Bär, F., Schweda, C.M.: Social-software-based support for enterprise architecture management processes. In: Fournier, F., Mendling, J. (eds.) Business Process Management Workshops—BPM 2014 International Workshops, Eindhoven, The Netherlands, 7–8 Sep 2014, Revised Papers, pp. 452–462. Springer (2014)Google Scholar
  31. 31.
    Codd, E.F.: Relational completeness of data base sublanguages. IBM Corporation (1972)Google Scholar
  32. 32.
    Beaumont, S., Gasser, D., Baumgarten, A.: Microsoft System Center 2012 Service Manager Cookbook. Packt Publishing, Birmingham (2012)Google Scholar
  33. 33.
    Bunch, C.: Automating vSphere with VMware vCenter Orchestrator. VMware Press (2012)Google Scholar
  34. 34.
    Hajlaoui, J.E., Hamdani, N.: Active data warehouse: Review, challenges and issues. In: 2014 World Symposium on Computer Applications and Research (WSCAR), pp. 1–6. IEEE (2014)Google Scholar
  35. 35.
    Bughin, J., Chui, M., Manyika, J.: Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly. 56 (2010)Google Scholar
  36. 36.
    LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manage. Rev. 52, 21–32 (2011)Google Scholar
  37. 37.
    Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36, 1165–1188 (2012)Google Scholar
  38. 38.
    Schmidt, R., Möhring, M.: Strategic alignment of cloud-based architectures for big data. In: Proceedings of the 17th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW). Vancouver, Canada (2013)Google Scholar
  39. 39.
    Mohanty, S., Jagadeesh, M., Srivatsa, H.: Big Data Imperatives: Enterprise “Big Data” Warehouse, “BI” Implementations and Analytics. Apress (2013)Google Scholar
  40. 40.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)CrossRefGoogle Scholar
  41. 41.
    Murthy, A.: Apache Hadoop YARN: moving beyond MapReduce and batch processing with Apache Hadoop 2. Pearson, Upper Saddle River, NJ (2014)Google Scholar
  42. 42.
    Xin, R.S., Crankshaw, D., Dave, A., Gonzalez, J.E., Franklin, M.J., Stoica, I.: GraphX: unifying data-parallel and graph-parallel analytics. arXiv:1402.2394 [cs] (2014)
  43. 43.
    Psaltis, G.: Streaming Data. Manning (2015)Google Scholar
  44. 44.
    Aier, S., Ahrens, M., Stutz, M., Bub, U.: Deriving SOA evaluation metrics in an enterprise architecture context. In: Service-Oriented Computing-ICSOC 2007 Workshops, pp. 224–233 (2009)Google Scholar
  45. 45.
    Vasconcelos, A., Sousa, P., Tribolet, J.: Information system architecture metrics: an enterprise engineering evaluation approach. Electron. J. Inf. Syst. Eval. 10, 91–122 (2007)Google Scholar
  46. 46.
    Weirich, P.: Decision space: Multidimensional utility analysis. Cambridge University Press (2001)Google Scholar
  47. 47.
    Leitch, G., Tanner, J.E.: Economic forecast evaluation: profits versus the conventional error measures. Am. Econ. Rev. 580–590 (1991)Google Scholar
  48. 48.
    Cao, L., Soofi, A.S.: Nonlinear deterministic forecasting of daily dollar exchange rates. Int. J. Forecast. 15, 421–430 (1999)CrossRefzbMATHGoogle Scholar
  49. 49.
    Faruk, D.Ö.: A hybrid neural network and ARIMA model for water quality time series prediction. Eng. Appl. Artif. Intell. 23, 586–594 (2010)CrossRefGoogle Scholar
  50. 50.
    Vogel, J.: Prognose von zeitreihen. Springer (2014)Google Scholar
  51. 51.
    Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)CrossRefzbMATHGoogle Scholar
  52. 52.
    Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: A tutorial. Computer, pp. 31–44 (1996)Google Scholar
  53. 53.
    Zurada, J.M.: Introduction to Artificial Neural Systems. West St, Paul (1992)Google Scholar
  54. 54.
    Schmidhuber, J.: Deep learning in neural networks: An overview. Neural Networks 61, 85–117 (2015)CrossRefGoogle Scholar
  55. 55.
    Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: The state of the art. Int. J. Forecast. 14, 35–62 (1998)CrossRefGoogle Scholar
  56. 56.
    Chow, G.C.: Tests of equality between sets of coefficients in two linear regressions. Econometrica J. Econometric Soc. 591–605 (1960)Google Scholar
  57. 57.
    Hansen, B.E.: Testing for parameter instability in linear models. J. Policy Model. 14, 517–533 (1992)CrossRefGoogle Scholar
  58. 58.
    Hofmann, M., Klinkenberg, R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications. CRC Press (2013)Google Scholar
  59. 59.
    Luftman, J., Kempaiah, R.: An update on business-IT alignment: “A line” has been drawn. MIS Q. Executive 6, 165–177 (2007)Google Scholar
  60. 60.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, pp. 207–216. ACM (1993)Google Scholar
  61. 61.
    Kotu, V.: Predictive Analytics and Data Mining: Concepts and Practice with Rapidminer. Elsevier, Waltham (2014)Google Scholar
  62. 62.
    Möhring, M., Schmidt, R., Härting, R.-C., Bär, F., Zimmermann, A.: Classification Framework for Context Data from Business Processes. In: Fournier, F., endling, J. (eds.) Business Process Management Workshops—BPM 2014 International Workshops, Eindhoven, The Netherlands, 7–8 Sep 2014, Revised Papers. pp. 440–445. Springer (2014)Google Scholar
  63. 63.
    Tan, A.: Text Mining: the state of the art and the challenges. In: Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases, pp. 65–70 (1999)Google Scholar
  64. 64.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37 (1996)Google Scholar
  65. 65.
    Simoudis, E.: Reality check for data mining. IEEE Intell. Syst. 11, 26–33 (1996)Google Scholar
  66. 66.
    Schmidt, R., Möhring, M., Härting, R.-C., Zimmermann, A., Heitmann, J., Blum, F.: Leveraging textual information for improving decision-making in the business process lifecycle. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies. Sorrent (2015)Google Scholar
  67. 67.
    Tan, P.-N., Blau, H., Harp, S., Goldman, R.: Textual data mining of service center call records. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 417–423. ACM (2000)Google Scholar
  68. 68.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. In: Soviet physics doklady, pp. 707–710 (1966)Google Scholar
  69. 69.
    Jordan, G.: Practical Neo4j. Apress, Berkeley (2014)CrossRefGoogle Scholar
  70. 70.
    Ryza, S. (ed.): Advanced Analytics with Spark: Paterns for Learning from Data at Scale. O’Reilly, Beijing (2015)Google Scholar
  71. 71.
    Kreps, J., Narkhede, N., Rao, J.: Kafka: A distributed messaging system for log processing. In: Proceedings of the NetDB (2011)Google Scholar
  72. 72.
    Markl, V.: Breaking the chains: On declarative data analysis and data independence in the big data era. Proceedings of the VLDB Endowment. 7, 1730–1733 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Munich University of Applied SciencesMunichGermany

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