iPRODICT - Intelligent Process Prediction based on Big Data Analytics

The major purpose of the iPRODICT research project is to operationalize industrial internet of things driven predictive and prescriptive analytics by embedding it to the operational processes. Particularly, within an interdisciplinary team of researchers and industry experts, we investigate an integration of diverse technologies to enable real time sensor data driven decision making for process improvements and optimization in the process industry. The case study concentrates on adaptation and optimization of both manufacturing and business processes by analyzing the quality of the semi-finished steel products proactively based on the sensor data obtained from the continuous casting process and chemical properties of the steel. In the underlying paper, we discussed three business process management specific use cases in the sensor-driven process industry, namely (i) business process instance adaptation, (ii) business process instanceto-instance adaptation and optimization and (iii) business process instance-tomodel adaptation. Furthermore, we discuss the components of the proposed predictive enterprise solution and their dependencies briefly and provide an insight to the challenges and lessons learnt over the diverse stages of the case study.

[1]  Klaus Moessner,et al.  Predicting complex events for pro-active IoT applications , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

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

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

[4]  Peter Loos,et al.  Event-Driven Business Process Management: where are we now?: A comprehensive synthesis and analysis of literature , 2014, Bus. Process. Manag. J..

[5]  Detlef D. Nauck,et al.  Sequential Clustering for Event Sequences and Its Impact on Next Process Step Prediction , 2014, IPMU.

[6]  Peter Loos,et al.  Towards an Extended Metamodel of Event-Driven Process Chains to Model Complex Event Patterns , 2015, ER Workshops.

[7]  Rainer von Ammon.  Event-Driven Business Process Management , 2018, Encyclopedia of Database Systems.

[8]  Bokyoung Kang,et al.  Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction , 2012, Expert Syst. Appl..

[9]  Avigdor Gal,et al.  Tuning complex event processing rules using the prediction-correction paradigm , 2009, DEBS '09.

[10]  Christian Janiesch,et al.  Beyond process monitoring: a proof-of-concept of event-driven business activity management , 2012, Bus. Process. Manag. J..

[11]  Alessandro Sperduti,et al.  Time and activity sequence prediction of business process instances , 2016, Computing.

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

[13]  J. Scott Armstrong,et al.  The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy , 2005 .

[14]  Imre KISS,et al.  ASSESSMENT OF SURFACE DEFECTS IN THE CONTINUOUSLY CAST STEEL , 2011 .

[15]  Detlef D. Nauck,et al.  A hybrid model for business process event and outcome prediction , 2017, Expert Syst. J. Knowl. Eng..

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

[17]  Ashutosh Tiwari,et al.  Business Process Analysis and Optimization: Beyond Reengineering , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  I. Yeoman.  Competing on analytics: The new science of winning , 2009 .

[19]  Ashutosh Tiwari,et al.  Business process improvement using multi-objective optimisation , 2006 .

[20]  Fabrizio Maria Maggi,et al.  Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes , 2015, BPM.

[21]  Peter Loos,et al.  Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques , 2015, Complex Adaptive Systems.

[22]  Bokyoung Kang,et al.  Periodic Performance Prediction for Real-time Business Process Monitoring , 2012, Ind. Manag. Data Syst..

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

[24]  Ashutosh Tiwari,et al.  Evolutionary Multi-objective Optimization of Business Processes , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[25]  Adrian Paschke,et al.  Rule-Based Event Processing and Reaction Rules , 2009, RuleML.

[26]  Nada R. Sanders,et al.  The efficacy of using judgmental versus quantitative forecasting methods in practice , 2003 .