publication . Preprint . Other literature type . Article . 2017

Predicting Process Behaviour using Deep Learning

Joerg Evermann; Jana-Rebecca Rehse; Peter Fettke;
Open Access English
  • Published: 01 Aug 2017
Abstract
Comment: 34 pages, 10 figures
Subjects
free text keywords: Computer Science - Learning, Statistics - Machine Learning, Arts and Humanities (miscellaneous), Information Systems and Management, Management Information Systems, Developmental and Educational Psychology, Information Systems, Business process, Recurrent neural network, Process modeling, Deep learning, Artificial neural network, Business process management, business.industry, business, Process mining, Business process discovery, Artificial intelligence, Computer science, Machine learning, computer.software_genre, computer
34 references, page 1 of 3

Bahrampour, S., Ramakrishnan, N., Schott, L., Shah, M., 2015. Comparative study of deep learning software frameworks. arXiv preprint arXiv:1511.06435. [OpenAIRE]

Breuker, D., Matzner, M., Delfmann, P., Becker, J., 2016. Comprehensible predictive models for business processes. MIS Quarterly In press.

Ceci, M., Lanotte, P. F., Fumarola, F., Cavallo, D. P., Malerba, D., 2014. Completion time and next activity prediction of processes using sequential pattern mining. In: Discovery Science - 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. pp. 49{61.

Choi, K., Fazekas, G., Sandler, M. B., 2016. Text-based LSTM networks for automatic music composition. CoRR abs/1604.05358.

Claes, J., Poels, G., 2012. Process mining and the prom framework: An exploratory survey. In: Business Process Management Workshops. Vol. 132 of Lecture Notes in Business Information Processing. Springer, pp. 187{198.

Gers, F. A., Schmidhuber, J., 2000. Recurrent nets that time and count. In: IJCNN (3). pp. 189{194. [OpenAIRE]

Graves, A., 2012. Supervised Sequence Labelling with Recurrent Neural Networks. Vol. 385 of Studies in Computational Intelligence. Springer.

Gre , K., Srivastava, R. K., Koutn k, J., Steunebrink, B. R., Schmidhuber, J., 2015. LSTM: A search space odyssey. CoRR abs/1503.04069.

Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Verlag, Berlin, Germany.

Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Computation 9 (8), 1735{1780.

Houy, C., Fettke, P., Loos, P., van der Aalst, W. M. P., Krogstie, J., 2010. Bpm-inthe-large - towards a higher level of abstraction in business process management. In: EGES/GISP. Vol. 334 of IFIP Advances in Information and Communication Technology. Springer, pp. 233{244.

Huang, A., Wu, R., 2016. Deep learning for music. CoRR abs/1606.04930.

Karpathy, A., 2016. The unreasonable e ectiveness of recurrent neural networks. Accessed 6 Dec, 2016.

Karpathy, A., Johnson, J., Li, F., 2015. Visualizing and understanding recurrent networks. CoRR abs/1506.02078. [OpenAIRE]

Lakshmanan, G. T., Shamsi, D., Doganata, Y. N., Unuvar, M., Khalaf, R., 2015. A markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97{126.

34 references, page 1 of 3
Abstract
Comment: 34 pages, 10 figures
Subjects
free text keywords: Computer Science - Learning, Statistics - Machine Learning, Arts and Humanities (miscellaneous), Information Systems and Management, Management Information Systems, Developmental and Educational Psychology, Information Systems, Business process, Recurrent neural network, Process modeling, Deep learning, Artificial neural network, Business process management, business.industry, business, Process mining, Business process discovery, Artificial intelligence, Computer science, Machine learning, computer.software_genre, computer
34 references, page 1 of 3

Bahrampour, S., Ramakrishnan, N., Schott, L., Shah, M., 2015. Comparative study of deep learning software frameworks. arXiv preprint arXiv:1511.06435. [OpenAIRE]

Breuker, D., Matzner, M., Delfmann, P., Becker, J., 2016. Comprehensible predictive models for business processes. MIS Quarterly In press.

Ceci, M., Lanotte, P. F., Fumarola, F., Cavallo, D. P., Malerba, D., 2014. Completion time and next activity prediction of processes using sequential pattern mining. In: Discovery Science - 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. pp. 49{61.

Choi, K., Fazekas, G., Sandler, M. B., 2016. Text-based LSTM networks for automatic music composition. CoRR abs/1604.05358.

Claes, J., Poels, G., 2012. Process mining and the prom framework: An exploratory survey. In: Business Process Management Workshops. Vol. 132 of Lecture Notes in Business Information Processing. Springer, pp. 187{198.

Gers, F. A., Schmidhuber, J., 2000. Recurrent nets that time and count. In: IJCNN (3). pp. 189{194. [OpenAIRE]

Graves, A., 2012. Supervised Sequence Labelling with Recurrent Neural Networks. Vol. 385 of Studies in Computational Intelligence. Springer.

Gre , K., Srivastava, R. K., Koutn k, J., Steunebrink, B. R., Schmidhuber, J., 2015. LSTM: A search space odyssey. CoRR abs/1503.04069.

Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Verlag, Berlin, Germany.

Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Computation 9 (8), 1735{1780.

Houy, C., Fettke, P., Loos, P., van der Aalst, W. M. P., Krogstie, J., 2010. Bpm-inthe-large - towards a higher level of abstraction in business process management. In: EGES/GISP. Vol. 334 of IFIP Advances in Information and Communication Technology. Springer, pp. 233{244.

Huang, A., Wu, R., 2016. Deep learning for music. CoRR abs/1606.04930.

Karpathy, A., 2016. The unreasonable e ectiveness of recurrent neural networks. Accessed 6 Dec, 2016.

Karpathy, A., Johnson, J., Li, F., 2015. Visualizing and understanding recurrent networks. CoRR abs/1506.02078. [OpenAIRE]

Lakshmanan, G. T., Shamsi, D., Doganata, Y. N., Unuvar, M., Khalaf, R., 2015. A markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97{126.

34 references, page 1 of 3
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publication . Preprint . Other literature type . Article . 2017

Predicting Process Behaviour using Deep Learning

Joerg Evermann; Jana-Rebecca Rehse; Peter Fettke;