Skip to main content
main-content

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

Erschienen in: Business & Information Systems Engineering 2/2020

19.07.2018 | Research Paper

A Novel Business Process Prediction Model Using a Deep Learning Method

verfasst von: Nijat Mehdiyev, Joerg Evermann, Peter Fettke

Erschienen in: Business & Information Systems Engineering | Ausgabe 2/2020

Einloggen, um Zugang zu erhalten
share
TEILEN

Abstract

The ability to proactively monitor business processes is a main competitive differentiator for firms. Process execution logs generated by process aware information systems help to make process specific predictions for enabling a proactive situational awareness. The goal of the proposed approach is to predict the next process event from the completed activities of the running process instance, based on the execution log data from previously completed process instances. By predicting process events, companies can initiate timely interventions to address undesired deviations from the desired workflow. The paper proposes a multi-stage deep learning approach that formulates the next event prediction problem as a classification problem. Following a feature pre-processing stage with n-grams and feature hashing, a deep learning model consisting of an unsupervised pre-training component with stacked autoencoders and a supervised fine-tuning component is applied. Experiments on a variety of business process log datasets show that the multi-stage deep learning approach provides promising results. The study also compared the results to existing deep recurrent neural networks and conventional classification approaches. Furthermore, the paper addresses the identification of suitable hyperparameters for the proposed approach, and the handling of the imbalanced nature of business process event datasets.
Literatur
Zurück zum Zitat Barga R, Fontama V, Tok WH, Cabrera-Cordon L (2015) Predictive analytics with Microsoft Azure machine learning. Apress, Berkely, CA CrossRef Barga R, Fontama V, Tok WH, Cabrera-Cordon L (2015) Predictive analytics with Microsoft Azure machine learning. Apress, Berkely, CA CrossRef
Zurück zum Zitat Bergstra JS, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. Advances in neural information processing systems. Granada, Spain, pp 2546–2554 Bergstra JS, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. Advances in neural information processing systems. Granada, Spain, pp 2546–2554
Zurück zum Zitat Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(1):281–305 Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(1):281–305
Zurück zum Zitat Bose RPJC, van der Aalst WMP, Žliobaitė I, Pechenizkiy M (2011) Handling concept drift in process mining. In: International conference on advanced information systems engineering, Springer, London, pp 391–405 Bose RPJC, van der Aalst WMP, Žliobaitė I, Pechenizkiy M (2011) Handling concept drift in process mining. In: International conference on advanced information systems engineering, Springer, London, pp 391–405
Zurück zum Zitat Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159 CrossRef Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159 CrossRef
Zurück zum Zitat Breuker D, Matzner M, Delfmann P, Becker J (2016) Comprehensible predictive models for business processes. MIS Q 40(4):1009–1034 CrossRef Breuker D, Matzner M, Delfmann P, Becker J (2016) Comprehensible predictive models for business processes. MIS Q 40(4):1009–1034 CrossRef
Zurück zum Zitat Candel A, Parmar V, LeDell E, Arora A (2016) Deep learning with h2o. H2O Inc, CA Candel A, Parmar V, LeDell E, Arora A (2016) Deep learning with h2o. H2O Inc, CA
Zurück zum Zitat Caragea C, Silvescu A, Mitra P (2012) Protein sequence classification using feature hashing. Proteome Sci 10(1):1–14 CrossRef Caragea C, Silvescu A, Mitra P (2012) Protein sequence classification using feature hashing. Proteome Sci 10(1):1–14 CrossRef
Zurück zum Zitat Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: 25th international conference on machine learning, ACM, Helsinki, pp 96–103 Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: 25th international conference on machine learning, ACM, Helsinki, pp 96–103
Zurück zum Zitat Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: 23rd international conference on machine learning. ACM, Pittsburgh, pp 161–168 Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: 23rd international conference on machine learning. ACM, Pittsburgh, pp 161–168
Zurück zum Zitat Da Silva NFF, Hruschka ER, Hruschka ER (2014) Tweet sentiment analysis with classifier ensembles. Decis Support Syst 66:170–179 CrossRef Da Silva NFF, Hruschka ER, Hruschka ER (2014) Tweet sentiment analysis with classifier ensembles. Decis Support Syst 66:170–179 CrossRef
Zurück zum Zitat Davenport TH, Harris JG (2007) Competing on analytics: the new science of winning. Harvard Business School Press, Boston Davenport TH, Harris JG (2007) Competing on analytics: the new science of winning. Harvard Business School Press, Boston
Zurück zum Zitat Di Francescomarino C, Dumas M, Federici M, et al (2016) Predictive business process monitoring framework with hyperparameter optimization. In: 28th international conference on advanced information systems engineering, Springer, Ljubljana, pp 361–376 Di Francescomarino C, Dumas M, Federici M, et al (2016) Predictive business process monitoring framework with hyperparameter optimization. In: 28th international conference on advanced information systems engineering, Springer, Ljubljana, pp 361–376
Zurück zum Zitat Duan L, Da Xu L (2012) Business intelligence for enterprise systems: a survey. IEEE Trans Ind Inform 8(3):679–687 CrossRef Duan L, Da Xu L (2012) Business intelligence for enterprise systems: a survey. IEEE Trans Ind Inform 8(3):679–687 CrossRef
Zurück zum Zitat Erhan D, Bengio Y, Courville A et al (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660 Erhan D, Bengio Y, Courville A et al (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660
Zurück zum Zitat Evermann J, Rehse J-R, Fettke P (2017) Predicting process behaviour using deep learning. Decis Support Syst 100:129–140 CrossRef Evermann J, Rehse J-R, Fettke P (2017) Predicting process behaviour using deep learning. Decis Support Syst 100:129–140 CrossRef
Zurück zum Zitat Folino F, Guarascio M, Pontieri L (2012) Discovering context-aware models for predicting business process performances. In: OTM confederated international conferences “on the move to meaningful internet systems”, Springer, Rome, pp 287–304 Folino F, Guarascio M, Pontieri L (2012) Discovering context-aware models for predicting business process performances. In: OTM confederated international conferences “on the move to meaningful internet systems”, Springer, Rome, pp 287–304
Zurück zum Zitat Forman G, Kirshenbaum E (2008) Extremely fast text feature extraction for classification and indexing. In: 17th ACM conference on information and knowledge management. ACM, Napa Valley, pp 1221–1230 Forman G, Kirshenbaum E (2008) Extremely fast text feature extraction for classification and indexing. In: 17th ACM conference on information and knowledge management. ACM, Napa Valley, pp 1221–1230
Zurück zum Zitat Ganchev K, Dredze M (2008) Small statistical models by random feature mixing. In: the ACL08 HLT workshop on mobile language processing, Columbus, OH, pp 19–20 Ganchev K, Dredze M (2008) Small statistical models by random feature mixing. In: the ACL08 HLT workshop on mobile language processing, Columbus, OH, pp 19–20
Zurück zum Zitat Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Q 37(2):337–356 CrossRef Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Q 37(2):337–356 CrossRef
Zurück zum Zitat Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newslett 11(1):10–18 CrossRef Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newslett 11(1):10–18 CrossRef
Zurück zum Zitat Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg, pp 878–887 Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg, pp 878–887
Zurück zum Zitat Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554 CrossRef Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554 CrossRef
Zurück zum Zitat Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507 CrossRef Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507 CrossRef
Zurück zum Zitat Huang C, Li Y, Change Loy C, Tang X (2016) Learning deep representation for imbalanced classification. In: IEEE conference on computer vision and pattern recognition, IEEE, Las Vegas, pp 5375–5384 Huang C, Li Y, Change Loy C, Tang X (2016) Learning deep representation for imbalanced classification. In: IEEE conference on computer vision and pattern recognition, IEEE, Las Vegas, pp 5375–5384
Zurück zum Zitat Kang B, Kim D, Kang S (2012a) Periodic performance prediction for real-time business process monitoring. Ind Manag Data Syst 112(1):4–23 CrossRef Kang B, Kim D, Kang S (2012a) Periodic performance prediction for real-time business process monitoring. Ind Manag Data Syst 112(1):4–23 CrossRef
Zurück zum Zitat Kang B, Kim D, Kang S-H (2012b) Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction. Expert Syst Appl 39(5):6061–6068 CrossRef Kang B, Kim D, Kang S-H (2012b) Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction. Expert Syst Appl 39(5):6061–6068 CrossRef
Zurück zum Zitat Lakshmanan GT, Shamsi D, Doganata YN et al (2015) A markov prediction model for data-driven semi-structured business processes. Knowl Inf Syst 42(1):97–126 CrossRef Lakshmanan GT, Shamsi D, Doganata YN et al (2015) A markov prediction model for data-driven semi-structured business processes. Knowl Inf Syst 42(1):97–126 CrossRef
Zurück zum Zitat Langford J, Li L, Strehl A (2007) Vowpal wabbit online learning project. Technical report, Yahoo! Langford J, Li L, Strehl A (2007) Vowpal wabbit online learning project. Technical report, Yahoo!
Zurück zum Zitat Larochelle H, Erhan D, Courville A et al (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: 24th international conference on machine larning, ACM, Corvallis, pp 473–480 Larochelle H, Erhan D, Courville A et al (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: 24th international conference on machine larning, ACM, Corvallis, pp 473–480
Zurück zum Zitat LaValle S, Lesser E, Shockley R et al (2011) Big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52:21–32 LaValle S, Lesser E, Shockley R et al (2011) Big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52:21–32
Zurück zum Zitat Le M, Gabrys B, Nauck D (2017) A hybrid model for business process event and outcome prediction. Expert Syst 34(5):e12079 CrossRef Le M, Gabrys B, Nauck D (2017) A hybrid model for business process event and outcome prediction. Expert Syst 34(5):e12079 CrossRef
Zurück zum Zitat Le M, Nauck D, Gabrys B, Martin T (2014) Sequential clustering for event sequences and its impact on next process step prediction. In: International conference on information processing and management of uncertainty in knowledge-based systems, Springer, Cádiz, pp 168–178 Le M, Nauck D, Gabrys B, Martin T (2014) Sequential clustering for event sequences and its impact on next process step prediction. In: International conference on information processing and management of uncertainty in knowledge-based systems, Springer, Cádiz, pp 168–178
Zurück zum Zitat LeCun YA, Bottou L, Orr GB, Müller KR (2012) Efficient backprop. Neural networks: tricks of the trade. Springer, Berlin, pp 9–50 CrossRef LeCun YA, Bottou L, Orr GB, Müller KR (2012) Efficient backprop. Neural networks: tricks of the trade. Springer, Berlin, pp 9–50 CrossRef
Zurück zum Zitat Leontjeva A, Conforti R, Di Francescomarino C, et al (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: International conference on business process management, Springer, Innsbruck, pp 297–313 Leontjeva A, Conforti R, Di Francescomarino C, et al (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: International conference on business process management, Springer, Innsbruck, pp 297–313
Zurück zum Zitat Márquez-Chamorro AE, Resinas M, Ruiz-Cortés A, Toro M (2017) Run-time prediction of business process indicators using evolutionary decision rules. Expert Syst Appl 87:1–14 CrossRef Márquez-Chamorro AE, Resinas M, Ruiz-Cortés A, Toro M (2017) Run-time prediction of business process indicators using evolutionary decision rules. Expert Syst Appl 87:1–14 CrossRef
Zurück zum Zitat Mehdiyev N, Evermann J, Fettke P (2017) A multi-stage deep learning approach for business process event prediction. In: IEEE 19th conference on business informatics, IEEE, Thessaloniki, pp 119–128 Mehdiyev N, Evermann J, Fettke P (2017) A multi-stage deep learning approach for business process event prediction. In: IEEE 19th conference on business informatics, IEEE, Thessaloniki, pp 119–128
Zurück zum Zitat Metzger A, Leitner P, Ivanovic D et al (2015) Comparing and combining predictive business process monitoring techniques. IEEE Trans Syst, Man, Cybern Syst 45(2):276–290 CrossRef Metzger A, Leitner P, Ivanovic D et al (2015) Comparing and combining predictive business process monitoring techniques. IEEE Trans Syst, Man, Cybern Syst 45(2):276–290 CrossRef
Zurück zum Zitat Rogge-Solti A, Weske M (2013) Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: International conference on service-oriented computing, Springer, Berlin, pp 389–403 Rogge-Solti A, Weske M (2013) Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: International conference on service-oriented computing, Springer, Berlin, pp 389–403
Zurück zum Zitat Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117 CrossRef Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117 CrossRef
Zurück zum Zitat Senderovich A, Di Francescomarino C, Ghidini C et al (2017) Intra and inter-case features in predictive process monitoring: a tale of two dimensions. In: International conference on business process management, Springer, Barcelona, pp 306–323 Senderovich A, Di Francescomarino C, Ghidini C et al (2017) Intra and inter-case features in predictive process monitoring: a tale of two dimensions. In: International conference on business process management, Springer, Barcelona, pp 306–323
Zurück zum Zitat Sun Z, Pambel F, Wang F (2015) Incorporating big data analytics into enterprise information systems. In: Information and communication technology: third IFIP TC 5/8 international conference, ICT-EurAsia 2015, and 9th IFIP WG 8.9 working conference, CONFENIS 2015, Springer, Daejeon, pp 300–309 Sun Z, Pambel F, Wang F (2015) Incorporating big data analytics into enterprise information systems. In: Information and communication technology: third IFIP TC 5/8 international conference, ICT-EurAsia 2015, and 9th IFIP WG 8.9 working conference, CONFENIS 2015, Springer, Daejeon, pp 300–309
Zurück zum Zitat Sun Y, Wong AKC, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(4):687–719 CrossRef Sun Y, Wong AKC, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(4):687–719 CrossRef
Zurück zum Zitat Tax N, Verenich I, La Rosa M, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: International conference on advanced information systems engineering, Springer, Essen, pp 477–492 Tax N, Verenich I, La Rosa M, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: International conference on advanced information systems engineering, Springer, Essen, pp 477–492
Zurück zum Zitat Tomović A, Janičić P, Kešelj V (2006) n-Gram-based classification and unsupervised hierarchical clustering of genome sequences. Comput Methods Programs Biomed 81(2):137–153 CrossRef Tomović A, Janičić P, Kešelj V (2006) n-Gram-based classification and unsupervised hierarchical clustering of genome sequences. Comput Methods Programs Biomed 81(2):137–153 CrossRef
Zurück zum Zitat Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231 CrossRef Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231 CrossRef
Zurück zum Zitat Unuvar M, Lakshmanan GT, Doganata YN (2016) Leveraging path information to generate predictions for parallel business processes. Knowl Inf Syst 47(2):433–461 CrossRef Unuvar M, Lakshmanan GT, Doganata YN (2016) Leveraging path information to generate predictions for parallel business processes. Knowl Inf Syst 47(2):433–461 CrossRef
Zurück zum Zitat van der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475 CrossRef van der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475 CrossRef
Zurück zum Zitat van Dongen BF, Crooy RA, van der Aalst WMP (2008) Cycle time prediction: when will this case finally be finished? In: OTM confederated international conferences “on the move to meaningful internet systems”, Springer, Monterey, pp 319–336 van Dongen BF, Crooy RA, van der Aalst WMP (2008) Cycle time prediction: when will this case finally be finished? In: OTM confederated international conferences “on the move to meaningful internet systems”, Springer, Monterey, pp 319–336
Zurück zum Zitat Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: 25th international conference on machine learning, ACM, Helsinki, pp 1096–1103 Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: 25th international conference on machine learning, ACM, Helsinki, pp 1096–1103
Zurück zum Zitat Vincent P, Larochelle H, Lajoie I et al (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408 Vincent P, Larochelle H, Lajoie I et al (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408
Zurück zum Zitat Wang S, Yao X (2012) Multiclass imbalance problems: analysis and potential solutions. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(4):1119–1130 CrossRef Wang S, Yao X (2012) Multiclass imbalance problems: analysis and potential solutions. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(4):1119–1130 CrossRef
Zurück zum Zitat Weinberger K, Dasgupta A, Langford J, et al (2009) Feature hashing for large scale multitask learning. In: Proceedings of the 26th annual international conference on machine learning – ICML’09, ACM, Montreal, pp 1–8 Weinberger K, Dasgupta A, Langford J, et al (2009) Feature hashing for large scale multitask learning. In: Proceedings of the 26th annual international conference on machine learning – ICML’09, ACM, Montreal, pp 1–8
Zurück zum Zitat Wickham H, Francois R (2015) dplyr: a grammar of data manipulation. R Package Version 04(1):20 Wickham H, Francois R (2015) dplyr: a grammar of data manipulation. R Package Version 04(1):20
Zurück zum Zitat Witt N, Seifert C (2017) Understanding the influence of hyperparameters on text embeddings for text classification tasks. In: International conference on theory and practice of digital libraries, Springer, Thessaloniki, pp 193–204 Witt N, Seifert C (2017) Understanding the influence of hyperparameters on text embeddings for text classification tasks. In: International conference on theory and practice of digital libraries, Springer, Thessaloniki, pp 193–204
Zurück zum Zitat Wu X, Kumar V, Quinlan JR et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37 CrossRef Wu X, Kumar V, Quinlan JR et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37 CrossRef
Metadaten
Titel
A Novel Business Process Prediction Model Using a Deep Learning Method
verfasst von
Nijat Mehdiyev
Joerg Evermann
Peter Fettke
Publikationsdatum
19.07.2018
Verlag
Springer Fachmedien Wiesbaden
Erschienen in
Business & Information Systems Engineering / Ausgabe 2/2020
Print ISSN: 2363-7005
Elektronische ISSN: 1867-0202
DOI
https://doi.org/10.1007/s12599-018-0551-3

Weitere Artikel der Ausgabe 2/2020

Business & Information Systems Engineering 2/2020 Zur Ausgabe

Premium Partner