Narrowing the Business-IT Gap in Process Performance Measurement

  • Han van der AaEmail author
  • Adela del-Río-Ortega
  • Manuel Resinas
  • Henrik Leopold
  • Antonio Ruiz-Cortés
  • Jan Mendling
  • Hajo A. Reijers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)


To determine whether strategic goals are met, organizations must monitor how their business processes perform. Process Performance Indicators (PPIs) are used to specify relevant performance requirements. The formulation of PPIs is typically a managerial concern. Therefore, considerable effort has to be invested to relate PPIs, described by management, to the exact operational and technical characteristics of business processes. This work presents an approach to support this task, which would otherwise be a laborious and time-consuming endeavor. The presented approach can automatically establish links between PPIs, as formulated in natural language, with operational details, as described in process models. To do so, we employ machine learning and natural language processing techniques. A quantitative evaluation on the basis of a collection of 173 real-world PPIs demonstrates that the proposed approach works well.


Performance measurement Process performance indicators Model alignment Natural language processing 


  1. 1.
    van der Aa, H., Leopold, H., Mannhardt, F., Reijers, H.A.: On the fragmentation of process information: challenges, solutions, and outlook. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) BPMDS 2015 and EMMSAD 2015. LNBIP, vol. 214, pp. 3–18. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  2. 2.
    van der Aa, H., Leopold, H., Reijers, H.A.: Detecting inconsistencies between process models and textual descriptions. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015, vol. 9253, pp. 90–105. Springer, Switzerland (2015)CrossRefGoogle Scholar
  3. 3.
    Achour, C.B.: Guiding scenario authoring1. Information Modelling and Knowledge Bases X 51, 152 (1999)Google Scholar
  4. 4.
    Dijkman, R.M., 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
  5. 5.
    Gal, A.: Uncertain schema matching. Synth. Lect. Data Manag. 3(1), 1–97 (2011)CrossRefzbMATHGoogle Scholar
  6. 6.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD 11(1), 10–18 (2009)CrossRefGoogle Scholar
  7. 7.
    Islam, A., Inkpen, D.: Second order co-occurrence pmi for determining the semantic similarity of words. In: Proceedings of the International Conference on Language Resources and Evaluation, Genoa, Italy, pp. 1033–1038 (2006)Google Scholar
  8. 8.
    Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceedings of the 41st Annual Meeting of the ACL, vol. 1, pp. 423–430. ACL (2003)Google Scholar
  9. 9.
    Klinkmüller, C., Weber, I., Mendling, J., Leopold, H., Ludwig, A.: Increasing recall of process model matching by improved activity label matching. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 211–218. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Kolb, P.: Disco: a multilingual database of distributionally similar words. In: Proceedings of KONVENS-2008, Berlin (2008)Google Scholar
  11. 11.
    Kovacic, A.: Business renovation: business rules (still) the missing link. Bus. Process Manag. J. 10(2), 158–170 (2004)CrossRefGoogle Scholar
  12. 12.
    Kronz, A.: Managing of process key performance indicators as part of the aris methodology. In: Scheer, A.W., Jost, W., Heß, H., Kronz, A. (eds.) Corporate Performance Management, pp. 31–44. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Kunze, M., Weidlich, M., Weske, M.: Behavioral similarity – a proper metric. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 166–181. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Luftman, J., Papp, R., Brier, T.: Enablers and inhibitors of business-it alignment. Commun. AIS 1(3es), 1–32 (1999)Google Scholar
  15. 15.
    Marshall, B., Chen, H., Madhusudan, T.: Matching knowledge elements in concept maps using a similarity flooding algorithm. Decis. Support Syst. 42(3), 1290–1306 (2006)CrossRefGoogle Scholar
  16. 16.
    Mendling, J., Reijers, H.A., Recker, J.: Activity labeling in process modeling: empirical insights and recommendations. Inf. Syst. 35(4), 467–482 (2010)CrossRefGoogle Scholar
  17. 17.
    Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI, vol. 6, p. 775–780 (2006)Google Scholar
  18. 18.
    Miller, G.A.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  19. 19.
    Momm, C., Malec, R., Abeck, S.: Towards a model-driven development of monitored processes. Wirtschaftsinformatik 2, 319–336 (2007)Google Scholar
  20. 20.
    Popova, V., Sharpanskykh, A.: Modeling organizational performance indicators. Inf. Syst. 35(4), 505–527 (2010)CrossRefGoogle Scholar
  21. 21.
    Quinlan, J.R.: C4. 5: Programs for Machine Learning. Elsevier, Amsterdam (2014)Google Scholar
  22. 22.
    del-Río-Ortega, A., Cabanillas, C., Resinas, M., Ruiz-Cortés, A.: PPINOT tool suite: a performance management solution for process-oriented organisations. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 675–678. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  23. 23.
    del–Río–Ortega, A., Gutiérrez, A.M., Durán, A., Resinas, M., Ruiz–Cortés, A.: Modelling service level agreements for business process outsourcing services. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 485–500. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  24. 24.
    del-Río-Ortega, A., Resinas, M., Cabanillas, C., Ruiz-Cortes, A.: On the definition and design-time analysis of process performance indicators. Inf. Syst. 38(4), 470–490 (2013)CrossRefGoogle Scholar
  25. 25.
    Rosemann, M.: Potential pitfalls of process modeling: part A. bus. process manag. j. 12(2), 249–254 (2006)CrossRefGoogle Scholar
  26. 26.
    Wetzstein, B., Ma, Z., Leymann, F.: Towards measuring key performance indicators of semantic business processes. In: Abramowicz, W., Fensel, D. (eds.) BIS, pp. 227–238. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Han van der Aa
    • 1
    Email author
  • Adela del-Río-Ortega
    • 2
  • Manuel Resinas
    • 2
  • Henrik Leopold
    • 1
  • Antonio Ruiz-Cortés
    • 2
  • Jan Mendling
    • 3
  • Hajo A. Reijers
    • 1
    • 4
  1. 1.Department of Computer SciencesVU University AmsterdamAmsterdamThe Netherlands
  2. 2.Dpto. de Lenguajes y Sistemas InformticosUniversity of SevilleSevilleSpain
  3. 3.Institute for Information BusinessWUViennaAustria
  4. 4.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands

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