Business Process Failure Prediction: a case study
Resumo
Business process monitoring aims at maintaining the reliability of process executions. However, the dynamic nature of business processes hinders a proactive scenario in which risk mitigation actions can occur before the facts that put the process at risk. Thus, some premises are necessary such as the identification of situations and patterns in historical data of the processes execution in order to characterize what determined the failures. In this paper, we address the problem of how to identify and detect patterns of behaviors that can lead the processes to a failure situation. As a solution, a combination of well-established techniques from Data and Process Mining fields are applied in a case study of an incident management process. The results obtained open possibilities to a proactive scenario.
Palavras-chave:
Data Mining, Business Process, Process Monitoring, Failure Prediction
Referências
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Maggi, F. M., Di Francescomarino, C., Dumas, M., and Ghidini, C. pp. 457–472. In M. Jarke, J. Mylopoulos, C. Quix, C. Rolland, Y. Manolopoulos, H. Mouratidis, and J. Horko (Eds.), Predictive Monitoring of Business Processes. Springer International Publishing, Cham, pp. 457–472, 2014.
Marquez-Chamorro, A. E., Resinas, M., and Ruiz-Cortes, A. Predictive monitoring of business processes: a survey, 2017.
Márquez-Chamorro, A. E., Resinas, M., Ruiz-Cortés, A., and Toro, M. Run-time prediction of business process indicators using evolutionary decision rules. Expert Systems with Applications vol. 87, pp. 1–14, 2017.
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Schapire, R. E. and Freund, Y. Boosting: Foundations and algorithms. MIT press, 2012.
Van Der Aalst, W. M. Business process management: a comprehensive survey. ISRN Software Engineering, 2013.
van der Aalst, W. M. Process Mining: Data Science in Action. Springer-Verlag Berlin Heidelberg, 2016.
Weske, M., Marco Montali, Ingo Weber, and Jan vom Brocke. Business Process Management. In Business Process Management, M. Weske, M. Montali, I. Weber, and J. vom Brocke (Eds.). Lecture Notes in Computer Science, vol. 11080. Springer International Publishing, 2018.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
Breuker, D., Matzner, M., Delfmann, P., and Becker, J. Comprehensible predictive models for business processes. MIS Quarterly 40 (4): 1009–1034, 2016.
del Río-Ortega, A., García, F., Resinas, M., Weber, E., Ruiz, F., and Ruiz-Cortés, A. pp. 193–209. In E. Dubois and K. Pohl (Eds.), Enriching Decision Making with Data-Based Thresholds of Process-Related KPIs. Springer International Publishing, Cham, pp. 193–209, 2017.
del Río-Ortega, A., Gutiérrez, A. M., Durán, A., Resinas, M., and Ruiz-Cortés, A. Modelling service level agreements for business process outsourcing services. In Advanced Information Systems Engineering, J. Zdravkovic, M. Kirikova, and P. Johannesson (Eds.). Springer International Publishing, Cham, pp. 485–500, 2015.
Fawcett, T. An introduction to roc analysis. Pattern recognition letters 27 (8): 861–874, 2006.
Günther, C. and van der Aalst, W. Fuzzy mining–adaptive process simplification based on multi-perspective metrics. Business Process Management, 2007.
Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning. Bayesian Forecasting and Dynamic Models vol. 1, pp. 1–694, 2009.
Hompes, B. F. A., Maaradji, A., La Rosa, M., Dumas, M., Buijs, J. C. A. M., and van der Aalst, W. M. P. Discovering Causal Factors Explaining Business Process Performance Variation. In CAiSE. Vol. 7328. pp. 177–192, 2017.
Kohavi, R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, 1995.
Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., and Maggi, F. M. Complex symbolic sequence encodings for predictive monitoring of business processes. In International Conference on Business Process Management. Springer, pp. 297–313, 2015.
Maggi, F. M., Di Francescomarino, C., Dumas, M., and Ghidini, C. pp. 457–472. In M. Jarke, J. Mylopoulos, C. Quix, C. Rolland, Y. Manolopoulos, H. Mouratidis, and J. Horko (Eds.), Predictive Monitoring of Business Processes. Springer International Publishing, Cham, pp. 457–472, 2014.
Marquez-Chamorro, A. E., Resinas, M., and Ruiz-Cortes, A. Predictive monitoring of business processes: a survey, 2017.
Márquez-Chamorro, A. E., Resinas, M., Ruiz-Cortés, A., and Toro, M. Run-time prediction of business process indicators using evolutionary decision rules. Expert Systems with Applications vol. 87, pp. 1–14, 2017.
Rudzajs, P. and Kirikova, M. Advanced Information Systems Engineering. In CAiSE 2018, J. Krogstie and H. A. Reijers (Eds.).
Lecture Notes in Computer Science, vol. 10816. Springer International Publishing, 2018.
Schapire, R. E. and Freund, Y. Boosting: Foundations and algorithms. MIT press, 2012.
Van Der Aalst, W. M. Business process management: a comprehensive survey. ISRN Software Engineering, 2013.
van der Aalst, W. M. Process Mining: Data Science in Action. Springer-Verlag Berlin Heidelberg, 2016.
Weske, M., Marco Montali, Ingo Weber, and Jan vom Brocke. Business Process Management. In Business Process Management, M. Weske, M. Montali, I. Weber, and J. vom Brocke (Eds.). Lecture Notes in Computer Science, vol. 11080. Springer International Publishing, 2018.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
Publicado
07/10/2019
Como Citar
MELLO, Pedro O. T.; REVOREDO, Kate; SANTORO, Flávia.
Business Process Failure Prediction: a case study. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE) , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 89-96.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2019.8793.
