Skip to main content
SpringerLink
Log in

Business alignment: using process mining as a tool for Delta analysis and conformance testing

  • Original Article
  • Published:
Requirements Engineering Aims and scope Submit manuscript

Abstract

Increasingly, business processes are being controlled and/or monitored by information systems. As a result, many business processes leave their “footprints” in transactional information systems, i.e., business events are recorded in so-called event logs. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. In this paper we focus on the potential use of process mining for measuring business alignment, i.e., comparing the real behavior of an information system or its users with the intended or expected behavior. We identify two ways to create and/or maintain the fit between business processes and supporting information systems: Delta analysis and conformance testing. Delta analysis compares the discovered model (i.e., an abstraction derived from the actual process) with some predefined processes model (e.g., the workflow model or reference model used to configure the system). Conformance testing attempts to quantify the “fit” between the event log and some predefined processes model. In this paper, we show that Delta analysis and conformance testing can be used to analyze business alignment as long as the actual events are logged and users have some control over the process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Note that in Table 1 we abstract from event types, i.e., we consider activities to be atomic. In real logs events typically correspond to the start or completion of an activity. This way it is possible to measure the duration of activity and to explicitly detect parallelism. Moreover, there are other event types related to failures, scheduling, delegations, etc. For simplicity we abstract from this in this paper. However, in our process mining tools we take event types into account

  2. The name of the organization is not given for reasons of confidentiality. We want to thank L. Maruster, R. Dorenbos, H.J. de Vries, H. Reijers, and A. in ’t Veld for their valuable support.

  3. An interesting question is how many events are required to correctly discover the process model. There is not a simple answer to that a priori. However, there are simple techniques to assess the completeness of the result, e.g., K-fold cross validation, which splits the event log into K parts and for each part it is verified whether adding it changes the result.

  4. The term “overfitting” may seem incorrect in this context because we are not really constructing a model. However, in this section we use it as an antonym for the term “underfitting”, i.e., the model allows for more behavior than the behavior that actually occurs in real-life.

References

  1. van der Aalst WMP, van Dongen BF, Herbst J, Maruster L, Schimm G, Weijters AJMM (2003) Workflow mining: a survey of issues and approaches. Data Knowl Eng 47(2):237–267

    Article  Google Scholar 

  2. van der Aalst WMP, Weijters AJMM (eds) (2004) Process mining, special issue of computers in industry, vol 53, Number 3. Elsevier, Amsterdam

  3. Bider I, Regev G, Soffer P (eds) (2004) In: Proceedings of the 5th workshop on business process modeling, development and support (BPMDS’04), vol 2 of Caise’04 Workshops. Riga Technical University, Latvia

  4. van der Aalst WMP (2004) Business alignment: using process mining as a tool for delta analysis. In: Grundspenkis J, Kirikova M (eds) Proceedings of the 5th workshop on business process modeling, development and support (BPMDS’04), vol 2 of Caise’04 Workshops. Riga Technical University, Latvia, pp 138–145

  5. Desel J, Reisig W, Rozenberg G (eds) Lectures on concurrency and petri nets, vol 3098 of Lecture Notes in Computer Science. Springer, Berlin Heidelberg New York

  6. van der Aalst WMP, Weijters AJMM, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Knowledge Data Eng 16(9):1128–1142

    Article  Google Scholar 

  7. Maruster L (2003) A machine learning approach to understand business processes. PhD Thesis, Eindhoven University of Technology, Eindhoven

  8. Bemmel J, Musen MA (1997) Handbook of medical informatics. Springer, Berlin Heidelberg New York

    Google Scholar 

  9. Coiera E (2003) Guide to health informatics. Arnold Publishers, Shawnee

    Google Scholar 

  10. Peleg M, Tu S, Bury J, Ciccarese P, Fox J, Greenes RA, Hall R, Johnson PD, Jones N, Kumar A, Miksch S, Quaglini S, Seyfang A, Shortliffe EH, Stefanelli M (2003) Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc 10(1):52–68

    Article  PubMed  Google Scholar 

  11. Fox J, Johns N, Rahmanzadeh A (1998) Disseminating medical knowledge: the PROforma approach. Artif Intell Med 14(1):157–182

    Article  PubMed  Google Scholar 

  12. Quaglini S, Stefanelli M, Lanzola G, Caporusso V, Panzarasa S (2001) Flexible guideline-based patient careflow systems. Artif Intell Med 22(1):65–80

    Article  PubMed  Google Scholar 

  13. de Clerq PA, Blom JA, Korsten HH, Hasman A (2004) Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif Intell Med 31(1):1–27

    Article  PubMed  Google Scholar 

  14. Athena P (2002) Flower user manual. Pallas Athena BV, Apeldoorn

    Google Scholar 

  15. Keller G, Teufel T (1998) SAP R/3 Process oriented implementation. Addison-Wesley, Reading

    Google Scholar 

  16. Rosemann M, van der Aalst WMP (2003) A configurable reference modelling language. QUT Technical report, FIT-TR-2003-05, Queensland University of Technology, Brisbane

  17. van der Aalst WMP (1998) The application of petri nets to workflow management. J Circuit Syst Comput 8(1):21–66

    Article  Google Scholar 

  18. van der Aalst WMP, Basten T (2001) Identifying commonalities and differences in object life cycles using behavioral inheritance. In: Colom JM, Koutny M (eds) Application and theory of petri nets 2001, vol 2075 of Lecture notes in computer science, Springer, Berlin Heidelberg New York, pp 32–52

  19. Basten T, van der Aalst WMP (2001) Inheritance of behavior. J Logic Algebraic Program 47(2):47–145

    Article  MATH  Google Scholar 

  20. van der Aalst WMP (2001) Exterminating the dynamic change bug: a concrete approach to support workflow change. Inf Syst Frontiers 3(3):297–317

    Article  Google Scholar 

  21. Ellis CA, Keddara K, Rozenberg G (1995) Dynamic change within workflow systems. In: Comstock N, Ellis C, Kling R, Mylopoulos J, Kaplan S (eds) Proceedings of the conference on organizational computing systems, Milpitas, California, August 1995. ACM SIGOIS, ACM Press, New York, pp 10–21

  22. Chan YE (2002) Why haven’t we mastered alignment?: the importance of the informal organization structure. MIS Q Executive 1(2):95–110

    Google Scholar 

  23. van der Aalst WMP, van Dongen BF (2002) Discovering workflow performance models from timed logs. In: Han Y, Tai S, Wikarski D (eds) International Conference on engineering and deployment of cooperative information systems (EDCIS 2002), vol 2480 of Lecture notes in computer science. Springer, Berlin Heidelberg New York, pp 45–63

  24. Agrawal R, Gunopulos D, Leymann F (1998) Mining process models from workflow logs. In: Sixth international conference on extending database technology, pp 469–483

  25. Cook JE, Wolf AL (1998) Discovering models of software processes from event-based data. ACM Trans Softw Eng Method 7(3):215–249

    Article  Google Scholar 

  26. Herbst J (2000) A machine learning approach to workflow management. In: Proceedings 11th European conference on machine learning, vol 1810 of Lecture Notes in Computer Science. Springer, Berlin Heidelberg New York, pp 183–194

  27. IDS Scheer (2002) ARIS process performance manager (ARIS PPM): measure, analyze and optimize your business process performance (whitepaper). IDS Scheer, Saarbruecken, Gemany. http://www.ids-scheer.com

  28. zur Mühlen M, Rosemann M (2000) Workflow-based process monitoring and controlling—technical and organizational issues. In: Sprague R (ed) Proceedings of the 33rd Hawaii International Conference on System Science (HICSS-33). IEEE Computer Society Press, Los Alamitos, California, pp 1–10

  29. Sayal M, Casati F, Dayal U, Shan MC (2002) Business process cockpit. In: Proceedings of 28th international conference on very large data bases (VLDB’02), Morgan Kaufmann, San fransisco, pp 880–883

  30. Weijters AJMM, van der Aalst WMP (2003) Rediscovering workflow models from event-based data using little thumb. Integr Comp-Aided Eng 10(2):151–162

    Google Scholar 

  31. Cook JE, Wolf AL (1999) Software process validation: quantitatively measuring the correspondence of a process to a model. ACM Trans Softw Eng Method 8(2):147–176

    Article  Google Scholar 

  32. Moreno JL (1934) Who shall survive? Nervous and Mental Disease Publishing Company, Washington DC

    Google Scholar 

  33. Bavelas AA (1948) A mathematical model for group structures. Hum Organ 7:16–30

    Google Scholar 

  34. Bernard HR, Killworth PD, McCarty C, Shelley GA, Robinson S (1990) Comparing four different methods for measuring personal social networks. Soc Netw 12:179–216

    Article  Google Scholar 

  35. Burt RS, Minor M (1983) Applied network analysis: a methodological introduction. Sage, Newbury Park

    Google Scholar 

  36. Feldman M (1987) Electronic mail and weak ties in organizations. Office Technol People 3:83–101

    Article  Google Scholar 

  37. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  38. Freeman LC (1979) Centrality in social networks: conceptual clarification. Soc Netw 1:215–239

    Article  Google Scholar 

  39. Nemati H, Barko CD (2003) Organizational data mining: leveraging enterprise data resources for optimal performance. Idea Group Publishing, Hershey

    Google Scholar 

  40. Scott J (1992) Social network analysis. Sage, Newbury Park

    Google Scholar 

  41. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge

    Google Scholar 

  42. van Glabbeek RJ, Weijland WP (1996) Branching time and abstraction in bisimulation semantics. J ACM 43(3):555–600

    Article  MathSciNet  Google Scholar 

  43. van der Aalst WMP (2003) Inheritance of business processes: a journey visiting four notorious problems. In: Ehrig H, Reisig W, Rozenberg G, Weber H (eds) Petri net technology for communication based systems, vol 2472 of Lecture Notes in Computer Science. Springer, Berlin Heidelberg New York, pp 383–408

  44. van der Aalst WMP, de Medeiros AKA (2004) Process mining and security: detecting anomalous process executions and checking process conformance. In: Busi N, Gorrieri R, Martinelli F (eds) Second International Workshop on security issues with petri nets and other computational models (WISP 2004), STAR, Servizio Tipografico Area della Ricerca, CNR Pisa, Italy, pp 69–84

Download references

Acknowledgements

The author would like to thank Ton Weijters, Boudewijn van Dongen, Ana Karla Alves de Medeiros, Minseok Song, Laura Maruster, Eric Verbeek, Monique Jansen-Vullers, Hajo Reijers, Michael Rosemann, Anne Rozinat, and Peter van den Brand for their on-going work on process mining techniques. Parts of this paper have been based on earlier papers with these researchers.

Author information

Authors and Affiliations

  1. Department of Technology Management, Eindhoven University of Technology, P.O. Box 513, 5600MB, Eindhoven, The Netherlands

    W. M. P. van der Aalst

Authors
  1. W. M. P. van der Aalst
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to W. M. P. van der Aalst.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aalst, W.M.P.v.d. Business alignment: using process mining as a tool for Delta analysis and conformance testing. Requirements Eng 10, 198–211 (2005). https://doi.org/10.1007/s00766-005-0001-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00766-005-0001-x

Keywords

Search

Navigation