Informatica

van der Aalst, W.M.P., Nakatumba, J., Rozinat, A., Russell, N. (2010). Business process simulation: how to get it right? In: Handbook on Business Process Managemen. Springer, Berlin, Heidelberg, pp. 313–338.

van der Aalst, W.M.P., Schonenberg, M.H., Song, M. (2011). Time prediction based on process mining. Information Systems, 36(2), 450–475.

van der Aalst, W.M.P., Adriansyah, A., Medeiros, A.K.A., Arcieri, F., Baier, T., Blickle, T., Bose, J.C., van den Brand, P., Brandtjen, R., Buijs, J., Burattin, A., Carmona, J., Castellanos, M., Claes, J., Cook, J., Costantini, N., Curbera, F., Damiani, E., de Leoni, M., Delias, P., van Dongen, B.F., Dumas, M., Dustdar, S., Fahland, D., Ferreira, D.R., Gaaloul, W., van Geffen, F., Goel, S., Günther, C., Guzzo, A., Harmon, P., ter Hofstede, A., Hoogland, J., Ingvaldsen, J.E., Kato, K., Kuhn, R., Kumar, A., La Rosa, M., Maggi, F., Malerba, D., Mans, R.S., Manuel, A., McCreesh, M., Mello, P., Mendling, J., Montali, M., Motahari-Nezhad, H.R., zur Muehlen, M., Munoz-Gama, J., Pontieri, L., Ribeiro, J., Rozinat, A., Seguel Pérez, H., Seguel Pérez, R., Sepúlveda, M., Sinur, J., Soffer, P., Song, M., Sperduti, A., Stilo, G., Stoel, C., Swenson, K., Talamo, M., Tan, W., Turner, C., Vanthienen, J., Varvaressos, G., Verbeek, E., Verdonk, M., Vigo, R., Wang, J., Weber, B., Weidlich, M., Weijters, T., Wen, L., Westergaard, M., Wynn, M. (2012). Process mining manifesto. In: Business Process Management Workshops. Springer, Berlin, Heidelberg, pp. 169–194.

Arroyo-Figueroa, G., Sucar, L.E. (1999). A temporal Bayesian network for diagnosis and prediction. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., pp. 13–20.

Darwiche, A. (2008). Chapter 11. Bayesian Networks. In: van Harmelen, Lifschitz V, F., Porter, B. (Eds.), Handbook of Knowledge Representation, Vol. 3. Elsevier, Amsterdam, pp. 467–509.

van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P. (2008). Cycle time prediction: when will this case finally be finished? In: Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp. 319–336.

van Dongen, B.F. (2015a). BPI Challenge 2012. Eindhoven University of Technology. Dataset.

van Dongen, B.F. (2015b). BPI Challenge 2015 Municipality 5. Eindhoven University of Technology.

Günther, C.W., Verbeek, E. (2014). XES Standard 2.0. Eindhoven.

Kellner, M.I., Madachy, R.J., Ra, D.M., Raffo, D.M. (1999). Software process simulation modeling: why? what? how? Journal of Systems and Software, 46(2), 91–105.

Martin, N., Depaire, B., Caris, A. (2015). The use of process mining in a business process simulation context: overview and challenges. In: CIDM 2014 2014 IEEE Symposium on Computational Intelligence and Data Mining, pp. 381–388.

de Leoni, M., van der Aalst, W.M.P. (2013). Data-aware process mining: discovering decisions in processes using alignments. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, pp. 1454–1461.

Liu, Y., Zhang, H., Li, C., Jiao, R.J. (2012). Workflow simulation for operational decision support using event graph through process mining. Decision Support Systems, 52(3), 685–697.

Ping, J., Chen, Y., Chen, B., Howboldt, K. (2010). A robust statistical analysis approach for pollutant loadings in urban rivers. Journal of Environmental Informatics, 16, 35–42.

Rogge-Solti, A., Mans, R.S., van der Aalst, W.M., Weske, M. (2013). Improving documentation by repairing event logs. In: IFIP Working Conference on The Practice of Enterprise Modeling. Springer, Berlin, Heidelberg, pp. 129–144.

Rogge-Solti, A., Kasneci, G. (2014). Temporal anomaly detection in business processes. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8659. Springer, pp. 234–249.

Rozinat, A., van der Aalst, W.M.P. (2016). Decision mining in business processes. Beta, Research School for Operations Management and Logistics, 2006.

Sarno, R., Dewandono, R.D., Ahmad, T., Naufal, M.F. (2015). Hybrid association rule learning and process mining for fraud detection. IAENG International Journal of Computer Science, 42, 59–72.

Savickas, T., Vasilecas, O. (2014). Bayesian belief network application in process mining. In: Proceedings of the 15th International Conference on Computer Systems and Technologies – CompSysTech ’14, Vol. 883. ACM, pp. 226–233.

van der Spoel, S., Van Keulen, M., Amrit, C. (2012). Process prediction in noisy data sets: a case study in a dutch hospital. In: International Symposium on Data-Driven Process Discovery and Analysis. Springer, Berlin, Heidelberg, pp. 60–83.

Sutrisnowati, R.A., Bae, H., Song, M. (2015). Bayesian network construction from event log for lateness analysis in port logistics. Computers & Industrial Engineering, 89, 53–66.

Verbeek, H.M.W., Buijs, J.C., van Dongen, B.F., van der Aalst, W.M. (2010). Xes, xesame, and prom 6. In: Forum at the Conference on Advanced Information Systems Engineering (CAiSE). Springer, Berlin, Heidelberg, pp. 60–75.

Verenich, I., Dumas, M., La Rosa, M., Maggi, F.M., di Francescomarino, C. (2016). Minimizing overprocessing waste in business processes via predictive activity ordering. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9694. Springer, pp. 186–202.