Designing and evaluating an interpretable predictive modeling technique for business processes

Lecture Notes in Business Information Processing

Volumn 202, Issue , 2015, Pages 541-553

  Breuker, Dominic ^a   Delfmann, Patrick ^a   Matzner, Martin ^a   Becker, Jörg ^a  

^a UNIVERSITY OF MÜNSTER   (Germany)

Author keywords

Data mining; Grammatical inference; Predictive modeling; Process mining

Indexed keywords

COMPUTATIONAL GRAMMARS; DATA MINING; ENTERPRISE RESOURCE MANAGEMENT; PETRI NETS;

BUSINESS PROCESS MANAGEMENT; GRAMMATICAL INFERENCES; PREDICTIVE MODELING; PREDICTIVE MODELS; PROBABILISTIC MODELING; PROCESS DISCOVERY; PROCESS MINING; RETROSPECTIVE ANALYSIS;

ADMINISTRATIVE DATA PROCESSING;

EID: 84929926065     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-15895-2_46     Document Type: Conference Paper

References (30)

1. van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
2. van der Aalst, W.M.P.: Process discovery: capturing the invisible. IEEE Comput. Intell. Mag. 5(1), 28-41 (2010)
3. van der Aalst, W.M.P., Pesic, M., Song, M.: Beyond process mining: from the past to present and future. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 38-52. Springer, Heidelberg (2010)
4. van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. J. 36(2), 450-475 (2011)
5. Kim, A., Obregon, J., Jung, J.-Y.: Constructing Decision Trees from Process Logs for Performer Recommendation. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013 Workshops. LNBIP, vol. 171, pp. 224-236. Springer, Heidelberg (2014)
6. de la Higuera, C.: A bibliographical study of grammatical inference. Pattern Recogn. 38, 1332-1348 (2005)
7. Verwer, S., Eyraud, R., de la Higuera, C.: PAutomaC: a PFA/HMM learning competition. Mach. Learn. J. (2013)
8. Shmuelli, G., Koppius, O.R.: Predictive analytics in information systems research. Manag. Inf. Syst. Q. 35(3), 553-572 (2011)
9. van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87-111 (2010)
10. de la Higuera, C.: Grammatical Inference. Cambride University Press, Cambridge (2010)
11. van der Aalst, W.M.P.: The application of petri nets to workflow management. J. Circuits Syst. Comput. 8(1), 21-66 (1998)
12. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. The MIT Press, Cambridge (2009)
13. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257-286 (1989)
14. Vidal, E., Thollard, F., de la Higuera, C., Casacuberta, F., Carrasco, R.C.: Probabilistic finite-state machines-part I. IEEE Trans. Pattern Anal. Mach. Intell. 27(7), 1013-1025 (2005)
15. Verwer, S., Eyraud, R., de la Higuera, C.: Results of the PAutomaC probabilistic automaton learning competition. In: 11th International Conference on Grammatical Inference, pp. 243-248 (2012)
16. Shibata, C., Yoshinaka, R.: Marginalizing out transition probabilities for several subclasses of Pfas. In: JMLR-Workshop Conference Proceedings, ICGI 2012, vol. 21, pp. 259-263 (2012)
17. Hulden, M.: Treba: efficient numerically stable EM for PFA. In: JMLR Workshop Conference Proceedings, vol. 21, pp. 249-253 (2012)
18. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning-Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009)
19. van der Aalst, W.M.P., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169-194. Springer, Heidelberg (2012)
20. Steck, H., Jaakkola, T.: On the dirichlet prior and bayesian regularization. Neural Inf. Process. Syst. 15, 1441-1448 (2002)
21. Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, Cambridge (2011)
22. Bishop, C.M.: A new framework for machine learning. In: Zurada, J.M., Yen, G.G., Wang, J. (eds.) WCCI 2008, Plenary/Invited Lectures. LNCS, vol. 5050, pp. 1-24. Springer, Heidelberg (2008)
23. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1-22 (1977)
24. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
25. Moon, T.K.: The expectation-maximization algorithm. IEEE Signal Process. Mag. 13(6), 47-60 (1996)
26. Murphy, K.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)
27. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Second International Symposium on Information Theory, pp. 267-281 (1973)
28. Cortadella, J., Kishinevsky, M., Kondratyev, A., Lavagno, L., Yakovlev, A.: Petrify: a tool for manipulating concurrent specifications and synthesis of asynchronous controllers. IEICE Trans. Inf. Syst. E80-D(3), 315-325 (1997)
29. van Dongen, B.F.: BPI Challenge 2012 (2012). http://www.win.tue.nl/bpi/2012/challenge
30. Adriansyah, A., Buijs, J.: Mining Process Performance from Event Logs: The BPI Challenge 2012 Case Study. BPM Center Report BPM-12-15 (2012). BPMcenter.org