A Study of Quality and Accuracy Trade-offs in Process Mining

Published Online:https://doi.org/10.1287/ijoc.1100.0444

References

  • Agrawal R., Gunopulos D., Leymann F., Schek H.-J., Alonso G., Saltor F., Ramos I. Mining process models from workflow logs. 6th Internat. Conf. Extending Database Tech. Proc., Vol. 1377 (1998) (Springer, Berlin) 467–483Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Chesani F., Mello P., Montali M., Riguzzi F., Sebastianis M., Storari S., Ardagna D., Mecella M., Yang J. Checking compliance of execution traces to business rules. Proc. 4th Workshop Bus. Process Intelligence (BPI 08), Vol. 17 (2009) (Springer, Berlin) 131–145Lecture Notes in Business Information ProcessingPart 2CrossrefGoogle Scholar
  • Curran T., Ladd A.SAP R/3 Business Blueprint: Understanding the Business Process Reference Model (2000) 2nd ed.(Prentice Hall, Upper Saddle River, NJ) Google Scholar
  • de Medeiros A. K. A., Weijters A. J. M. M., van der Aalst W. M. P. Genetic process mining: An experimental evaluation. Data Mining Knowledge Discovery (2007) 14(2):245–304CrossrefGoogle Scholar
  • de Medeiros A. K. A., Guzzo A., Greco G., van der Aalst W. M. P., Weijters A. J. M. M., van Dongen B. F., Saccà D., ter Hofstede A., Benatallah B., Paik H.-Y. Process mining based on clustering: A quest for precision. BPM Internat. Workshops, Vol. 4928 (2008) (Springer, Berlin) 17–29Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Dustdar S., Hoffmann T., van der Aalst W. Mining of ad-hoc business processes with TeamLog. Data Knowledge Engrg. (2005) 55(2):129–158CrossrefGoogle Scholar
  • Greco G., Guzzo A., Pontieri L., Saccà D., Dai H., Srikant R., Zhang C. Mining expressive process models by clustering workflow traces. Proc. 8th Pacific-Asia Conf. Knowledge Discovery Data Mining, Vol. 3056 (2004) (Springer, Berlin) 52–62Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Greco G., Guzzo A., Pontieri L., Saccà D. Discovering expressive process models by clustering log traces. IEEE Trans. Knowledge Data Engrg. (2006) 18(8):1010–1027CrossrefGoogle Scholar
  • Günther C. W., van der Aalst W. M. P., Alonso G., Dadam P., Rosemann M. Fuzzy mining: Adaptive process simplification based on multi-perspective metrics. Proc. 5th Internat. BPM Conf., Vol. 4714 (2007) (Springer, Berlin) 328–343Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Herbst J., Karagiannis D. Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. Proc. 9th Internat. Workshop Database Expert Systems Appl. (1998) (IEEE Computer Society, Washington, DC) 745CrossrefGoogle Scholar
  • Herbst J., Karagiannis D. An inductive approach to the acquisition and adaptation of workflow models. Proc. IJCAI'99 Workshop Intelligent Workflow Process Management, Stockholm (1999) (AAAI Press, Menlo Park, CA) 52–57Google Scholar
  • Huang Z., Kumar A., Ardagna D., Mecella M., Yang J. New quality metrics for evaluating process models. Proc. 4th Workshops Bus. Process Intelligence (BPI 08), Vol. 17 (2009) (Springer, Berlin) 164–170Lecture Notes in Business Information ProcessingPart 2CrossrefGoogle Scholar
  • Lindland O. I., Sindre G., Sølvberg A. Understanding quality in conceptual modeling. IEEE Software (1994) 11(2):42–49CrossrefGoogle Scholar
  • Klein M., Bernstein A. Towards high-precision service retrieval. IEEE Internet Comput. (2004) 8(1):30–36CrossrefGoogle Scholar
  • Mans R. S., Schonenberg M. H., Song M., van der Aalst W. M. P., Bakker P. J. M., Fred A., Filipe J., Gamboa H. Application of process mining in healthcare—A case study in a Dutch hospital. BIOSTEC 2008. Communications in Computer and Information Science (2008) 25(Springer, Berlin) 425–438CrossrefGoogle Scholar
  • Măruşter L., Weijters A. J., Aalst W. M., Bosch A. A rule-based approach for process discovery: Dealing with noise and imbalance in process logs. Data Mining Knowledge Discovery (2006) 13(1):67–87CrossrefGoogle Scholar
  • Mendling J., Simon C., Eder J., Dustdar S. Business process design by view integration. Business Process Management Workshops, Vol. 4103 (2006) (Springer, Berlin) 55–64Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Mendling J., Reijers H. A., Cardoso J., Alonso G., Dadam P., Rosemann M. What makes process models understandable? Proc. 5th Internat. BPM Conf., Vol. 4714 (2007) (Springer, Berlin) 48–63Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Rozinat A., van der Aalst W. M. P. Conformance checking of processes based on monitoring real behavior. Inform. Systems (2008) 33(1):64–95CrossrefGoogle Scholar
  • Rozinat A., de Medeiros A. K. A., Günther C. W., Weijters A. J. M. M., van der Aalst W. M. P. Towards an evaluation framework for process mining algorithms. (2007) . BPM Center Report BPM-07-06, Business Process Management CenterGoogle Scholar
  • Rozinat A., de Medeiros A. K. A., Günther C. W., Weijters A. J. M. M., van der Aalst W. M. P., ter Hofstede A., Benatallah B., Paik H.-Y. The need for a process mining evaluation framework in research and practice. BPM Internat. Workshops, Vol. 4928 (2008) (Springer, Berlin) 84–89Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Schimm G., Liddle S. W., Mayr H. C., Thalheim B. Generic linear business process modeling. Workshops Conceptual Modeling Approaches E-Business World Wide Web Conceptual Modeling: Conceptual Modeling E-Business Web, Vol. 1921 (2000) (Springer, Berlin) 31–39Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Schimm G., ter Hofstede A., Weske M. Mining most specific workflow models from event-based data. Internat. Conf. Bus. Process Management, Vol. 2678 (2003) (Springer, Berlin) 25–40Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Silva R., Zhang J., Shanahan J. G., Grossman R. L., Bayardo R., Bennett K., Vaidya J. Probabilistic workflow mining. Internat. Conf. Knowledge Discovery Data Mining (2005) Chicago(ACM, New York) 275–284CrossrefGoogle Scholar
  • Song M., van der Aalst W. M. P. Supporting process mining by showing events at a glance. Proc. 7th Annual Workshop Inform. Technology Systems (2007) MontrealGoogle Scholar
  • Song M., Günther C. W., van der Aalst W. M. P., Ardagna D., Mecella M., Yang J. Trace clustering in process mining. Proc. 4th Workshop Bus. Process Intelligence (BPI 08), Vol. 17 (2009) (Springer, Berlin) 109–120Lecture Notes in Business Information ProcessingPart 2CrossrefGoogle Scholar
  • Spearman C. The proof and measurement of association between two things. Amer. J. Psych. (1904) 15(1):72–101CrossrefGoogle Scholar
  • Tan P.-N., Steinbach M., Kumar V.Introduction to Data Mining (2006) (Addison Wesley, Reading, MA) Google Scholar
  • van der Aalst W. M. P., Reijers H. A., Song M. Discovering social networks from event logs. Comput. Supported Cooperative Work (2005) 14(6):549–593CrossrefGoogle Scholar
  • van der Aalst W. M. P., Weijters A. J. M. M., Märuşter L. Workflow mining: Discovering process models from event logs. IEEE Trans. Knowledge Data Engrg. (2004a) 16(9):1128–1142CrossrefGoogle Scholar
  • van der Aalst W. M. P., ter Hofstede A. H. M., Kiepuszewski B., Barros A. P. Workflow patterns. Distributed Parallel Databases (2004b) 14(1):5–51CrossrefGoogle Scholar
  • van der Aalst W. M. P., van Dongen B. F., Herbst J., Maruster L., Schimm G., Weijters A. J. M. M. Workflow mining: A survey of issues and approaches. Data Knowledge Engrg. (2003) 47(2):237–267CrossrefGoogle Scholar
  • van der Aalst W. M. P., Reijers H. A., Weijters A. J. M. M., van Dongen B. F., de Medeiros A. K. A., Song M., Verbeek H. M. W. Business process mining: An industrial application. Inform. Systems (2007a) 32(5):713–732CrossrefGoogle Scholar
  • van der Aalst W. M. P., van Dongen B. F., Günther C. W., Mans R. S., de Medeiros A. K. A., Rozinat A., Rubin V., Song M., Verbeek H. M. W., Weijters A. J. M. M., Kleijin J., Yakovlev A. ProM 4.0: Comprehensive support for real process analysis. 28th Internat. Conf. Appl. Theory Petri Nets, Vol. 4546 (2007b) (Springer, Berlin) 484–494Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • van der Werf J. M. E. M., van Dongen B. F., Hurkens C. A. J., Serebrenik A., van Hee K. M., Valk R. Process discovery using integer linear programming. Proc. 29th Internat. Conf. Appl. Theory Petri Nets, Vol. 5062 (2008) (Springer, Berlin) 368–387Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • van Dongen B. F., van der Aalst W. M. P., Atzeni P., Chu W., Lu H., Zhou S., Ling T. W. Multi-phase process mining: Building instance graphs. Proc. Internat. Conf. Conceptual Modeling, Vol. 3288 (2004) (Springer, Berlin) 362–376Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • van Dongen B. F., van der Aalst W. M. P. Multi-phase process mining: Aggregating instance graphs into EPCs and Petri nets. 2nd Internat. Workshop Appl. Petri Nets Coordination, Workflow Bus. Process Management (PNCWB 2005) (2005) 35–58Google Scholar
  • van Dongen B. F., de Medeiros A. K. A., Wen L., Jensen K., van der Aalst W. M. P. Process mining: Overview and outlook of Petri net discovery algorithms. Transactions on Petri Nets and Other Models of Concurrency II, Vol. 5460 (2009) (Springer, Berlin) 225–242Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • van Dongen B. F., de Medeiros A. K. A., Verbeek H. M. W. M., Weijters A. J. M., van der Aalst W. M. P., Ciardo G., Darondeau P. The ProM framework: A new era in process mining tool support. Proc. 26th Internat. Conf. Appl. Theory Petri Nets, Vol. 3536 (2005) (Springer, Berlin) 444–454Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Weijters A. J. M. M., van der Aalst W. M. P. Rediscovering workflow models from event-based data using little thumb. Integrated Comput.-Aided Engrg. (2003) 10(2):151–162CrossrefGoogle Scholar
  • Wen L., Wang J., Sun J., Zhou X., Li J., Shen H. T., Kitsuregawa M., Zhang Y. Detecting implicit dependencies between tasks from event logs. Asia-Pacific Web Conf. Frontiers of WWW Res. Development, Vol. 3841 (2006) (Springer, Berlin) 591–603Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Yang W.-S., Hwang S.-Y. A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems Appl. (2006) 31(1):56–68CrossrefGoogle Scholar
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