Treatment of lack of information in process mining


  • Raykenler Yzquierdo-Herrera Informatic Science University
  • Rogelio Silverio-Castro Informatic Science University
  • Manuel Lazo-Cortés Informatic Science University



lack of information, process mining, trace, event log


Process mining is a research discipline including discovery, monitoring and improvement of real processes by extracting knowledge from event logs readily available in today's information systems. Most process mining algorithms assume that the traces are complete and free of noise. In reality, this assumption is rarely met. The lack of information affects the structure and understanding of the model discovered. The paper proposes a set of steps to estimate the lack of information in the traces used in processes mining. Align traces during the pre-processing stage is proposed as part of the estimation in order to detect possible situations in which lack of information is manifested. From detected situations, missing information is estimated and a new record is generated in correspondence to the event, which can be used by different process discovery algorithms. Finally we discuss the experimental results obtained from implementing the approach.

= 95 veces | PDF (ESPAÑOL (ESPAÑA))
= 33 veces|


Download data is not yet available.


C. Hentrich, Z. Uwe. Service Integration Patterns for Invoking Services from Business Processes. In Proceedings of 12th European Conference on Pattern Languages of Programs (EuroPLoP 2007). Irsee, Germany. 2007. pp. 1-45.

R. Agrawal, D. Gunopulos, F. Leymann. Mining Process Models from Workflow Logs. In 6th International Conference on Extending Database Technology. Ed. Springer-Verlag. London, UK. 1998. pp. 469-483.

J. Cook, A. Wolf, “Discovering Models of Software Processes from Event-Based Data,” ACM Transactions on Software Engineering and Methodology. Vol. 7. 1998. pp. 215-249.

W. Van der Aalst. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Ed. Springer-Verlag. Heidelberg, Germany. 2011. pp. 352.

W. Van der Aalst, A. Adriansyah, A. de Medeiros, F. Arcieri, T. Baier, T. Blickle, J. Chandra. “Business Process Management Workshops”. Lecture Notes in Business Information Processing. Vol. 99. 2011. pp. 167-168.

C. Günther, W. van der Aalst. “Fuzzy Mining: Adaptive Process Simplification Based on Multi-Perspective Metrics”. Lecture Notes in Computer Science. Vol. 4714. 2007. pp. 328-343.

W. van der Aalst, A. Weijters, “Process Mining: A Research Agenda.” Special Issue of Computers in Industry. Vol. 53. 2004. pp. 231-244.

R. Farkhady, S. Aali. “A Probabilistic Approach for Process Mining in Incomplete and Noisy Logs”. Lecture Notes in Engineering and Computer Science. Vol. 2188. 2011. pp. 415-420.

A. Adriansyah, B. van Dongen, W. van der Aalst. Conformance Checking Using Cost-Based Fitness Analysis. In EDOC ‘11 Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference, Helsinki, Finland. Ed. IEEE Computer Society. Washington DC, USA. 2011. pp. 55-64.

J. Muñoz, J. Carmona. A fresh look at precision in process conformance. Ed. Springer-Verlag. Hoboken, NJ, USA. 2010. pp. 211-226.

W. Van der Aalst. On the Representational Bias in Process Mining. In Proceedings of the 2011 IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises. Paris, France. 2011. pp. 2-7.

M. Song, W. Van der Aalst. Supporting process mining by showing events at a glance. In K. Chari & A. Kumar (Eds.). Proceeding of the Seventeenth Annual Workshop on Information Technologies and Systems (WITS’07). Montreal, Canada. December 8-9. 2007. pp. 139-145.

R. Bose, W. Van der Aalst. “Process diagnostics using trace alignment: Opportunities, issues and challenges.” Inf. Syst. Vol. 37. 2012. pp. 117-141.

L. Ly, C. Indiono, J. Mangler, S. Rinderle. “Data Transformation and Semantic Log Purging for Process Mining”. Lecture Notes in Computer Science. Vol. 7328. 2012. pp. 238-253.

W. Van der Aalst, H. Beer, B. Dongen. “Process mining and verification of properties: An approach based on temporal logic.” Lecture Notes in Computer Science. Vol. 3761. 2005. pp. 130-147.

W. Van. der Aalst, B. Van Dongen, C. Günther, A. Rozinat, E. Verbeek, A. Weijters. ProM: the process mining toolkit. In A.K. Alves de Medeiros & B. Weber (Eds.), Proceedings of the BPM 2009 Demonstration Track. Ulm, Germany. 2009. pp. 1-4.

H.. Verbeek, J. Buijs, B. van Dongen, W. van der Aalst. ProM6: The Process Mining Toolkit. Proceeding of the Proceedings of the Business Process Management 2010 Demonstration Track. Hoboken NJ, USA. 2010. Vol. 615. pp. 34-39.

A. de Medeiros. Genetic Process Mining. PhD. Thesis. Technische Universiteit Eindhoven. Eindhoven, Netherlands. 2006. pp. 384.

A. Tiwari, C. Turner, B. Majeed. “A review of business process mining: state-of-the-art and future trends.” Business Process Management. Vol. 14. 2008. pp. 5-22

R. Yzquierdo, R. Silverio, M. Lazo, A. Torres. “Diagnóstico de proceso basado en el descubrimiento de subprocesos.” Revista Ingeniería Industrial. Vol. 33. 2012. pp. 133-141.

A. Rozinat, W. Van der Aalst. “Conformance checking of processes based on monitoring real behavior.” Inf. Syst. Vol. 33. 2008. pp. 64-95.

R. Van Arendonk. A Benchmark Set for Process Discovery Algorithms. Master Thesis. Eindhoven University of Technology. Eindhoven, Netherlands. 2011. pp. 69.

J. Weerdt, M. Backer, J. Vanthienen, B. Baesens. “A critical evaluation study of model-log metrics in process discovery”. Lecture Notes in Business Information Processing. Vol. 66. 2011. pp. 158-169.

A. Adriansyah, B. Van Dongen, W. Van der Aalst. “Towards Robust Conformance Checking”. Lecture Notes in Business Information Processing. Vol. 66. 2011. pp. 122-133.

A. Rozinat, W. Van der Aalst. “Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models.” Lecture Notes in Computer Science. Vol. 3812. 2006. pp. 163-176.

L. Wen, J. Wang, W. Van der Aalst, Z. Wang, J. Sun. “A Novel Approach for Process Mining Based on Event Types.” Journal of Intelligent Information Systems. Vol. 32. 2009. pp. 163-190.

J. Werf, B. Dongen, C. Hurkens, A. Serebrenik. Process Discovery Using Integer Linear Programming. In Proceedings of the 29th international conference on Applications and Theory of Petri Nets. Xi’an, China. 2008. pp. 368-387.

W. Conover. Practical nonparametric statistics. 2nd ed. Ed. John Wiley & Sons. New York, US. 1998. pp. 332-467.



How to Cite

Yzquierdo-Herrera, R., Silverio-Castro, R., & Lazo-Cortés, M. (2014). Treatment of lack of information in process mining . Revista Facultad De Ingeniería Universidad De Antioquia, (69), 67–78.

Most read articles by the same author(s)

Similar Articles

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 > >> 

You may also start an advanced similarity search for this article.