Structural Feature Selection for Event Logs

  • Markku HinkkaEmail author
  • Teemu Lehto
  • Keijo Heljanko
  • Alexander Jung
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)


We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms’ run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection.


Automatic business process discovery Process mining Prediction Classification Machine learning Clustering Feature selection 



We want to thank QPR Software Plc for funding our research. Financial support of Academy of Finland projects 139402 and 277522 is acknowledged.


  1. 1.
    Bennett, K.P., Campbell, C.: Support vector machines: hype or hallelujah? SIGKDD Explor. 2(2), 1–13 (2000)CrossRefGoogle Scholar
  2. 2.
    Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Abstractions in process mining: a taxonomy of patterns. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 159–175. Springer, Heidelberg (2009). CrossRefGoogle Scholar
  3. 3.
    Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Discovering signature patterns from event logs. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, Singapore, 16–19 April 2013, pp. 111–118. IEEE (2013)Google Scholar
  4. 4.
    Conforti, R., de Leoni, M., Rosa, M.L., van der Aalst, W.M.P., ter Hofstede, A.H.M.: A recommendation system for predicting risks across multiple business process instances. Decis. Support Syst. 69, 1–19 (2015)CrossRefGoogle Scholar
  5. 5.
    Covões, T.F., Hruschka, E.R., de Castro, L.N., Santos, Á.M.: A cluster-based feature selection approach. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 169–176. Springer, Heidelberg (2009). CrossRefGoogle Scholar
  6. 6.
    Ding, C.H.Q., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3(2), 185–206 (2005)CrossRefGoogle Scholar
  7. 7.
    Francescomarino, C.D., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. CoRR, abs/1506.01428 (2015)Google Scholar
  8. 8.
    Granitto, P.M., Furlanello, C., Biasioli, F., Gasperi, F.: Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemom. Intell. Lab. Syst. 83(2), 83–90 (2006)CrossRefGoogle Scholar
  9. 9.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)zbMATHGoogle Scholar
  10. 10.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. Royal Stat. Soc. Ser. C (Applied Statistics) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  11. 11.
    Hinkka, M.: Support materials for articles (2017). Accessed 13 Mar 2017
  12. 12.
    Hinkka, M., Lehto, T., Heljanko, K.: Assessing big data SQL frameworks for analyzing event logs. In: 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016, Heraklion, Crete, Greece, 17–19 February 2016, pp. 101–108. IEEE Computer Society (2016)Google Scholar
  13. 13.
    Lehto, T., Hinkka, M., Hollmén, J.: Focusing business improvements using process mining based influence analysis. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNBIP, vol. 260, pp. 177–192. Springer, Cham (2016). CrossRefGoogle Scholar
  14. 14.
    Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297–313. Springer, Cham (2015). CrossRefGoogle Scholar
  15. 15.
    Liaw, A., Wiener, M.: Classification and regression by randomforest. R news 2(3), 18–22 (2002)Google Scholar
  16. 16.
    Meyer, P.E.: Information-theoretic variable selection and network inference from microarray data. Ph.D. thesis. Université Libre de Bruxelles (2008)Google Scholar
  17. 17.
    Nguyen, H., Dumas, M., La Rosa, M., Maggi, F.M., Suriadi, S.: Mining business process deviance: a quest for accuracy. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 436–445. Springer, Heidelberg (2014). Google Scholar
  18. 18.
    Ogutu, J.O., Piepho, H.-P., Schulz-Streeck, T.: A comparison of random forests, boosting and support vector machines for genomic selection. In: BMC Proceedings, vol. 5, no. 3, p. S11 (2011)Google Scholar
  19. 19.
    Teinemaa, I., Dumas, M., Maggi, F.M., Di Francescomarino, C.: Predictive business process monitoring with structured and unstructured data. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 401–417. Springer, Cham (2016). CrossRefGoogle Scholar
  20. 20.
    Thompson, K.: Programming techniques: regular expression search algorithm. Commun. ACM 11(6), 419–422 (1968)CrossRefzbMATHGoogle Scholar
  21. 21.
    Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. Royal Stat. Soc. Ser. B (Methodological) 58(1), 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  22. 22.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)zbMATHGoogle Scholar
  23. 23.
    Van Dongen, B.: Real-Life Event Logs - Hospital Log (2011).
  24. 24.
    Van Dongen, B.: BPI Challenge 2014. Rabobank Nederland (2014).
  25. 25.
    Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T.A., Vapnik, V.: Feature selection for SVMs. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA, pp. 668–674. MIT Press, Cambridge (2000)Google Scholar
  26. 26.
    Zeng, Y., Luo, J., Lin, S.: Classification using Markov blanket for feature selection. In: The 2009 IEEE International Conference on Granular Computing, GrC 2009, Lushan Mountain, Nanchang, China, 17–19 August 2009, pp. 743–747. IEEE Computer Society (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Markku Hinkka
    • 1
    • 2
    Email author
  • Teemu Lehto
    • 1
    • 2
  • Keijo Heljanko
    • 1
    • 3
  • Alexander Jung
    • 1
  1. 1.Department of Computer Science, School of ScienceAalto UniversityEspooFinland
  2. 2.QPR Software PlcHelsinkiFinland
  3. 3.HIIT Helsinki Institute for Information TechnologyEspooFinland

Personalised recommendations