Using Convolutional Neural Networks for Predictive Process Analytics

Predictive process monitoring has recently become one of the main enablers of data-driven insights in process mining. As an application of predictive analytics, process prediction is mainly concerned with predicting the evolution of running traces based on models extracted from historical event logs. This paper presents a process mining approach, which uses convolutional neural networks to equip the execution scenario of a business process with a means to predict the next activity in a running trace. The basic idea is to convert the temporal data enclosed in the historical event log of a business process into spatial data so as to treat them as images. To this purpose, every trace of the event log is first transformed into the set of its prefix traces (i.e. sequences of events that represent the prefix of a trace). These prefix traces are mapped into 2D image-like data structures. Created spatial data are finally used to train a Convolutional Neural Network, in order to learn a deep learning model capable to predict the next activity (i.e. the activity associated to the event occurring after the last event in the considered prefix trace). This predictive deep model can be employed as a powerful service to support participants in performing business processes since it guarantees a higher utilization by acting proactively in anticipation. Preliminary tests with two benchmark logs are carried out to investigate the viability of the proposed approach.

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

[2]  Wil M. P. van der Aalst,et al.  Trace Clustering in Process Mining , 2008, Business Process Management Workshops.

[3]  Marlon Dumas,et al.  Predictive Business Process Monitoring with LSTM Neural Networks , 2016, CAiSE.

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

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

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Donato Malerba,et al.  Business Event Forecasting , 2015 .

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Boudewijn F. van Dongen,et al.  Cycle Time Prediction: When Will This Case Finally Be Finished? , 2008, OTM Conferences.

[10]  Wil M. P. van der Aalst,et al.  Process mining: discovering and improving Spaghetti and Lasagna processes , 2011, CIDM 2011.

[11]  Musaed Alhussein,et al.  EEG Pathology Detection Based on Deep Learning , 2019, IEEE Access.

[12]  Gerald Penn,et al.  Convolutional Neural Networks for Speech Recognition , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[13]  J.A. Anderson,et al.  Neurocomputing: Foundations of Research@@@Neurocomputing 2: Directions for Research , 1992 .

[14]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[15]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[16]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[17]  Donato Malerba,et al.  Process Mining to Forecast the Future of Running Cases , 2013, NFMCP.

[18]  Mathias Weske,et al.  Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays , 2013, ICSOC.

[19]  Wil M. P. van der Aalst,et al.  Time prediction based on process mining , 2011, Inf. Syst..

[20]  Bhanu Prasad,et al.  Speech, Audio, Image and Biomedical Signal Processing using Neural Networks , 2008, Studies in Computational Intelligence.

[21]  Donato Malerba,et al.  A Co-Training Strategy for Multiple View Clustering in Process Mining , 2016, IEEE Transactions on Services Computing.

[22]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[23]  Wil M. P. van der Aalst,et al.  Process Mining , 2016, Springer Berlin Heidelberg.

[24]  Michelangelo Ceci,et al.  Distributed Learning of Process Models for Next Activity Prediction , 2018, IDEAS.

[25]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

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

[27]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[28]  Xiang Zhang,et al.  Character-level Convolutional Networks for Text Classification , 2015, NIPS.