A systematic literature review on state-of-the-art deep learning methods for process prediction

Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.

[1]  Max Mühlhäuser,et al.  Unsupervised Anomaly Detection in Noisy Business Process Event Logs Using Denoising Autoencoders , 2016, DS.

[2]  Fabrizio Maria Maggi,et al.  Outcome-Oriented Predictive Process Monitoring , 2017, ACM Trans. Knowl. Discov. Data.

[3]  Sheetal Rathi,et al.  Comprehensive Survey on Deep Learning Approaches in Predictive Business Process Monitoring , 2020 .

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

[5]  Minseok Song,et al.  Predicting performances in business processes using deep neural networks , 2020, Decis. Support Syst..

[6]  Donato Malerba,et al.  Using Convolutional Neural Networks for Predictive Process Analytics , 2019, 2019 International Conference on Process Mining (ICPM).

[7]  Chiara Di Francescomarino Predictive Business Process Monitoring , 2019, Encyclopedia of Big Data Technologies.

[8]  Max Mühlhäuser,et al.  BINet: Multivariate Business Process Anomaly Detection Using Deep Learning , 2018, BPM.

[9]  Fabrizio Maria Maggi,et al.  Predictive Monitoring of Business Processes , 2013, CAiSE.

[10]  Fabrizio Maria Maggi,et al.  Predictive Process Monitoring Methods: Which One Suits Me Best? , 2018, BPM.

[11]  Alessandro Sperduti,et al.  LSTM networks for data-aware remaining time prediction of business process instances , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[12]  Jana-Rebecca Rehse,et al.  Predicting process behaviour using deep learning , 2016, Decis. Support Syst..

[13]  Oscar González Rojas,et al.  Learning Accurate LSTM Models of Business Processes , 2019, BPM.

[14]  Marlon Dumas,et al.  Outcome-Oriented Predictive Process Monitoring: Review and Benchmark , 2017 .

[15]  Miltos Petridis,et al.  Business Process Workflow Mining Using Machine Learning Techniques for the Rail Transport Industry , 2018, SGAI Conf..

[16]  Maximilian Röglinger,et al.  Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction , 2020, Business & Information Systems Engineering.

[17]  Massimo Mecella,et al.  Automated Discovery of Process Models from Event Logs: Review and Benchmark , 2017, IEEE Transactions on Knowledge and Data Engineering.

[18]  Hongming Cai,et al.  Predicting the Next Process Event Using Convolutional Neural Networks , 2018, 2018 IEEE International Conference on Progress in Informatics and Computing (PIC).

[19]  Houshang Darabi,et al.  Decay Replay Mining to Predict Next Process Events , 2019, IEEE Access.

[20]  Pearl Brereton,et al.  Systematic literature reviews in software engineering - A systematic literature review , 2009, Inf. Softw. Technol..

[21]  Antonio Ruiz-Cortés,et al.  Predictive Monitoring of Business Processes: A Survey , 2018, IEEE Transactions on Services Computing.

[22]  Annalisa Appice,et al.  Activity Prediction of Business Process Instances with Inception CNN Models , 2019, AI*IA.

[23]  Fabrizio Maria Maggi,et al.  Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring , 2019, ACM Trans. Intell. Syst. Technol..

[24]  Peter Fettke,et al.  A Novel Business Process Prediction Model Using a Deep Learning Method , 2018, Business & Information Systems Engineering.

[25]  Irene Teinemaa,et al.  An interdisciplinary comparison of sequence modeling methods for next-element prediction , 2018, Software and Systems Modeling.

[26]  Andreas Metzger,et al.  Considering Non-sequential Control Flows for Process Prediction with Recurrent Neural Networks , 2018, 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA).

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

[28]  Peter Fettke,et al.  A Multi-stage Deep Learning Approach for Business Process Event Prediction , 2017, 2017 IEEE 19th Conference on Business Informatics (CBI).

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

[30]  Chengfei Liu,et al.  Outcome-Oriented Predictive Process Monitoring with Attention-Based Bidirectional LSTM Neural Networks , 2019, 2019 IEEE International Conference on Web Services (ICWS).

[31]  Andreas Metzger,et al.  Risk-Based Proactive Process Adaptation , 2017, ICSOC.

[32]  Alexander Jung,et al.  Classifying Process Instances Using Recurrent Neural Networks , 2018, Business Process Management Workshops.

[33]  Aditya K. Ghose,et al.  Memory-Augmented Neural Networks for Predictive Process Analytics , 2018, ArXiv.

[34]  Hyerim Bae,et al.  Predictive Business Process Monitoring – Remaining Time Prediction using Deep Neural Network with Entity Embedding , 2019, Procedia Computer Science.

[35]  Fabrizio Maria Maggi,et al.  An Eye into the Future: Leveraging A-priori Knowledge in Predictive Business Process Monitoring , 2017, BPM.

[36]  Stefan Jablonski,et al.  Deep Learning Process Prediction with Discrete and Continuous Data Features , 2018, ENASE.

[37]  Klaus Pohl,et al.  Proactive Process Adaptation Using Deep Learning Ensembles , 2019, CAiSE.

[38]  Jianmin Wang,et al.  MM-Pred: A Deep Predictive Model for Multi-attribute Event Sequence , 2019, SDM.

[39]  Andreas Metzger,et al.  Predictive Business Process Monitoring Considering Reliability Estimates , 2017, CAiSE.

[40]  Javier Fabra,et al.  On the Use of Log-Based Model Checking, Clustering and Machine Learning for Process Behavior Prediction , 2018, 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS).

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

[42]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[43]  Marcello La Rosa,et al.  Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction , 2020, BPM.

[44]  Fabrizio Maria Maggi,et al.  Temporal stability in predictive process monitoring , 2018, Data Mining and Knowledge Discovery.