Predicting process behaviour using deep learning

Abstract Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

[1]  Alex Graves,et al.  Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.

[2]  Allen Huang,et al.  Deep Learning for Music , 2016, ArXiv.

[3]  Yurdaer N. Doganata,et al.  Leveraging path information to generate predictions for parallel business processes , 2015, Knowledge and Information Systems.

[4]  Jörg Becker,et al.  Comprehensible Predictive Models for Business Processes , 2016, MIS Q..

[5]  Detlef D. Nauck,et al.  A Hybrid Model for Business Process Event Prediction , 2012, SGAI Conf..

[6]  Wil M. P. van der Aalst,et al.  Beyond Process Mining: From the Past to Present and Future , 2010, CAiSE.

[7]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[8]  Mark B. Sandler,et al.  Text-based LSTM networks for Automatic Music Composition , 2016, ArXiv.

[9]  Yurdaer N. Doganata,et al.  A markov prediction model for data-driven semi-structured business processes , 2013, Knowledge and Information Systems.

[10]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[11]  Martin Bichler,et al.  Open Research in Business and Information Systems Engineering , 2016, Bus. Inf. Syst. Eng..

[12]  Shuliang Wang,et al.  Data Mining and Knowledge Discovery , 2005, Mathematical Principles of the Internet.

[13]  Geoffrey E. Hinton,et al.  Generating Text with Recurrent Neural Networks , 2011, ICML.

[14]  D. Holdstock.  Past, present--and future? , 2005, Medicine, conflict, and survival.

[15]  Lukás Burget,et al.  Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  Peter Loos,et al.  BPM-in-the-Large - Towards a Higher Level of Abstraction in Business Process Management , 2010, EGES/GISP.

[17]  Michelangelo Ceci,et al.  Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining , 2014, Discovery Science.

[18]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[19]  Geert Poels,et al.  Process Mining and the ProM Framework: An Exploratory Survey , 2012, Business Process Management Workshops.

[20]  Alan R. Hevner,et al.  POSITIONING AND PRESENTING DESIGN SCIENCE RESEARCH FOR MAXIMUM IMPACT 1 , 2013 .

[21]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[22]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[23]  Jana-Rebecca Rehse,et al.  A Deep Learning Approach for Predicting Process Behaviour at Runtime , 2016, Business Process Management Workshops.

[24]  Mohak Shah,et al.  Comparative Study of Deep Learning Software Frameworks , 2015, 1511.06435.

[25]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[26]  A. J. M. M. Weijters,et al.  Flexible Heuristics Miner (FHM) , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[27]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Jürgen Schmidhuber,et al.  Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[29]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[30]  Hod Lipson,et al.  Understanding Neural Networks Through Deep Visualization , 2015, ArXiv.

[31]  Xinlei Chen,et al.  Visualizing and Understanding Neural Models in NLP , 2015, NAACL.

[32]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[33]  Fei-Fei Li,et al.  Visualizing and Understanding Recurrent Networks , 2015, ArXiv.

[34]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[35]  Wojciech Zaremba,et al.  Recurrent Neural Network Regularization , 2014, ArXiv.

[36]  Boudewijn F. van Dongen,et al.  Replaying history on process models for conformance checking and performance analysis , 2012, WIREs Data Mining Knowl. Discov..

[37]  Alexander M. Rush,et al.  Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks , 2016, ArXiv.