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On the gap between reality and registration: a business event analysis classification framework

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Abstract

This paper presents a business event analysis classification framework, based on five business criteria. As a result, we are able to distinguish thirteen event types distributed over four categories, i.e. truthful, invisible, false and unobserved events. Currently, several of these event types are not commonly dealt with in business process management (BPM) and analytics (BPA) research. Based on the proposed framework we situate the different BPM and BPA research areas and indicate the potential issues for each field. A business case is elaborated to demonstrate the relevance of the event classification framework.

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References

  1. Buhl HU, Röglinger M, Stöckl S, Braunwarth KS (2011) Value orientation in process management research: gap and contribution to economically well-founded decisions in process management. Bus Inf Syst Eng 3(3):163–172

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. zur Mühlen M, Shapiro R (2010) Business process analytics. In: Handbook on business process management: strategic alignment, governance, people and culture

  4. van der Aalst WMP, Reijers HA, Weijters AJMM, van Dongen BF, de Medeiros AK, Song M, Verbeek HMW (2007) Business process mining: an industrial application. Inf Syst 32(5):713–732

    Article  Google Scholar 

  5. Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2007) A design science research methodology for information systems research. J manag inf syst 24(3):45–77

    Article  Google Scholar 

  6. Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28:75–105

    Google Scholar 

  7. De Nicola A, Missikoff M, Navigli R (2009) A software engineering approach to ontology building. Inf Syst 34(2):258–275

    Article  Google Scholar 

  8. Bariff ML, Ginzberg MJ (1982) MIS and the behavioral sciences: research patterns and prescriptions. ACM 14(1):19–26

    Google Scholar 

  9. Weijters T, van der Aalst W, de Medeiros AK (2006) Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Eindhoven

    Google Scholar 

  10. van der Aalst W, Weijters AJMM (2004) Process mining: a research agenda. Comput Ind 53(3):231–244

    Article  Google Scholar 

  11. Gangemi A, Guarino N, Masolo C, Oltramari A (2012) Sweetening ontologies with DOLCE. Springer, Berlin

    Google Scholar 

  12. Guizzardi G (2005) Ontological foundations for structural conceptual models. Universal Press, The Netherlands

    Google Scholar 

  13. Geerts GL, McCarthy WE (1999) An accounting object infrastructure for knowledge-based enterprise models. IEEE Intell Syst Their Appl 14(4):89–94

    Article  Google Scholar 

  14. Scheer AW, Thomas O, Adam O (2005) Process modeling using event-driven process Chains. Wiley-Interscience, Hoboken

    Book  Google Scholar 

  15. Chandy K, Schulte W (2009) Event processing: designing it systems for agile companies. McGraw-Hill Inc., New York

    Google Scholar 

  16. Moges HT, Dejaeger K, Lemahieu W, Baesens B (2012) A Multidimensional analysis of data quality for credit risk management: new insights and challenges. Inf Manag 50(1):43–58

    Article  Google Scholar 

  17. Jones D, Bench-Capon T, Visser P (1998) Methodologies for ontology development. In: IFIP world computer congress No 15, pp 62–75

  18. Uschold M, King M (1995) Towards a methodology for building ontologies. Paper presented at the Workshop on Basic Ontological Issues in Knowledge Sharing, held in conjunction with IJCAI ‘95

  19. Uschold M (1996) Building ontologies: Towards a unified methodology. Paper presented at the Expert Systems ‘96, the 16th Annual Conference of the British Computer Society Specialist Group on Expert Systems, Cambridge, UK

  20. Vanthienen J, Wets G (1994) From decision tables to expert system shells. Data Knowl Eng 13(3):265–282

    Article  Google Scholar 

  21. de Medeiros AKA, van Dongen BF, van der Aalst W (2004) Process mining: Extending the alpha-algorithm to Mine Short Loops. Eindhoven University of Technology, Eindhoven

    Google Scholar 

  22. Thomson C, Gheorghe M (2009) Measurements of Fidelity from Partial Process Descriptions (Initial Results). Report, Department of Computer Science, University of Sheffield

  23. Swinnen J, Vanhoof K, Hannes E (2010) Querying event logs: discovering non-events in event logs. 2010 international conference on Intelligent Systems and Knowledge Engineering (ISKE), Hangzhou, pp 349–354. doi:10.1109/ISKE.2010.5680850

  24. Clarke EM, Grumberg O, Peled DA (1999) Model checking. The MIT Oress, Cambridge, Massachusetts and London

    Google Scholar 

  25. van der Aalst WMP, De Beer HT, van Dongen BF (2005) Process mining and verification of properties: an approach based on temporal logic. Inf Syst J 3760:130–147

    Google Scholar 

  26. Rozinat A, van der Aalst WMP (2008) Conformance checking of processes based on monitoring real behavior. Inf Syst 33(1):64–95

    Article  Google Scholar 

  27. Goedertier S, Martens D, Vanthienen J, Baesens B (2009) Robust process discovery with artificial negative events. J Mach Learn Res 10:1305–1340

    Google Scholar 

  28. vanden Broucke SKLM, De Weerdt J, Baesens B, Vanthienen J (2012) Improved artificial negative event generation to enhance process event logs. Paper presented at the International Conference on Advanced Information Systems Engineering (CAiSE’12), Gdansk (Poland)

  29. Vanden Broucke SKLM, De Weerdt J, Vanthienen J, Baesens B (2013) Determining process model precision and generalization with weighted artificial negative events. IEEE Transactions on Knowledge and Data Engineering 99

  30. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley, Boston

    Google Scholar 

  31. Vom Brocke J, Rosemann M (2010) Handbook on business process management: strategic alignment, governance, People and Culture. Springer, Berlin

    Book  Google Scholar 

  32. Van der Aalst WM (2011) Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin

    Google Scholar 

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

    Article  Google Scholar 

  34. Goedertier S, Martens D, Baesens B, Haesen R, Vanthienen J (2008) Process mining as first-order classification learning on logs with negative events. In: ter Hofstede A, Benatallah B, Paik HY (eds) Business Process Management Workshops: BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers. Springer, Berlin Heidelberg, pp 42–53

  35. Ferreira HM, Ferreira DR (2006) An integrated life cycle for workflow management based on learning and planning. Int J Co-op Inf Syst 15(4):485–505

    Article  Google Scholar 

  36. Goedertier S, De Weerdt J, Martens D, Vanthienen J, Baesens B (2011) Process discovery in event logs: an application in the telecom industry. Appl Soft Comput 11(2):1697–1710

    Article  Google Scholar 

  37. Georgakopoulos D, Hornick M, Sheth A (1995) An overview of workflow management: from process modeling to workflow automation infrastructure. Distrib Parallel Databases 3(2):119–153

    Article  Google Scholar 

  38. Claes J, Poels G (2011) Integrating computer log files for process mining: a genetic algorithm inspired technique. Paper presented at the International conference on Advanced Information Systems Engineering, London

  39. Claes J, Poels G (2010) Merging computer log files for process mining: an artificial immune system technique. Paper presented at the 9th International Conference on Business Process Management (BPM 2011), Clermont-Ferrand, France

  40. De Pauw W, Hoch R, Huang Y (2007) Discovering Conversations in Web Services Using Semantic Correlation Analysis. Paper presented at the IEEE International Conference on Web Services (ICWS 2007)

  41. Motahari-Nezhad HR, Saint-Paul R, Casati F, Benatallah B (2011) Event correlation for process discovery from web service interaction logs. Int J Very Large Data Bases 20(3):417–444

    Article  Google Scholar 

  42. Green PF, Best PJ, Indulska M, Rowlands T (2005) Information systems audit and control issues with enterprise management systems: qualitative evidence. Aust Account Rev 15(3):68–77

    Google Scholar 

  43. De Beer HT (2004) The LTL-checker plugins: a reference manual. Eindhoven University of Technology, Eindhoven. http://www.processmining.org/_media/documentation/conformance/ltlchecker-manual.pdf. Accessed 28 Sep 2016

  44. van der Aalst WMP, van Hee KM, Werf JM, Verdonk M (2010) Auditing 2.0: using process mining to support tomorrow’s auditor. Computer 43(3):90–93

    Article  Google Scholar 

  45. Verbeek HMW, Buijs JCAM, van Dongen BF, van der Aalst WMP (2010) Prom 6: The process mining toolkit. Paper presented at the BPM Demonstration Track

  46. Rubin V, Günther CW, Van Der Aalst WMP, Kindler E, Van Dongen BF, Schäfer W (2007) Process mining framework for. Software processes Software Process Dynamics and Agility. Springer, Berlin, pp 169–181

    Book  Google Scholar 

  47. Bang-Jensen J, Gutin G (2007) Diagraphs: theory, algorithms and applications. Springer-Verlag, Springer Monographs in Mathematics, London

    Google Scholar 

  48. de Medeiros AKA, van der Aalst W, Pedrinaci C (2008) Semantic process mining tools: core building blocks. Paper presented at the 16th European Conference on Information Systems

  49. Montali M (2010) Specification and verification of declarative open interaction models: A logic-based approach. Database management & information retrieval. Springer, Berlin, p 56

    Book  Google Scholar 

Download references

Acknowledgments

This work has been supported by the KU Leuven research council for financial support under grand OT/10/010 and the Flemish Research Council for financial support under Odysseus grant B.0915.09.

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Authors and Affiliations

  1. Department of Decision Sciences and Information Management, KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium

    Seppe K. L. M. vanden Broucke, Filip Caron, Jasmien Lismont, Jan Vanthienen & Bart Baesens

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  1. Seppe K. L. M. vanden Broucke
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  2. Filip Caron
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  3. Jasmien Lismont
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  4. Jan Vanthienen
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  5. Bart Baesens
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Correspondence to Seppe K. L. M. vanden Broucke.

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vanden Broucke, S.K.L.M., Caron, F., Lismont, J. et al. On the gap between reality and registration: a business event analysis classification framework. Inf Technol Manag 17, 393–410 (2016). https://doi.org/10.1007/s10799-016-0262-8

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