Process mining is an emerging research field which deals with discovering, monitoring and improving business processes by analyzing and mining data in the form of event logs. Event logs can be extracted by most of the existing enterprise information systems. Predictive business process monitoring is a sub-field of process mining and deals with predictive analytics models on event log data that incorporate Machine Learning (ML) algorithms and deal with various objectives of process instances, such as: next activity, remaining time, costs, and risks. Existing research works on predictions about next activities are scarce. At the same time, Automated Machine Learning (AutoML) has not been investigated in the predictive business process monitoring domain. Therefore, based on its promising results in other domains and type of data, we propose an approach for next activity prediction based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method for AutoML. The evaluation results demonstrate that automating the design and optimization of ML pipelines without the need for human intervention, apart from making accessible ML to non-ML experts (in this case, the process owners and the business analysts), also provides higher prediction accuracy comparing to other approaches in the literature.
Van Der AalstW. Process mining: Overview and opportunities. ACM Transactions on Management Information Systems (TMIS). 2012 Jul 1; 3(2): 1-7.
2.
Van Der AalstWvan der AalstW. Data science in action. Springer Berlin Heidelberg. 2016.
3.
StierleMBrunkJWeinzierlSZilkerSMatznerMBeckerJ. Bringing light into the darkness-A systematic literature review on explainable predictive business process monitoring techniques.
4.
Di FrancescomarinoCGhidiniCMaggiFMMilaniF. Predictive process monitoring methods: Which one suits me best? InInternational Conference on Business Process Management. 2018 Aug 11; 462-479. Cham: Springer International Publishing.
5.
HeXZhaoKChuX. AutoML: A survey of the state-of-the-art. Knowledge-based systems. 2021 Jan 5; 212: 106622.
6.
KarmakerSKHassanMMSmithMJXuLZhaiCVeeramachaneniK. Automl to date and beyond: Challenges and opportunities. ACM Computing Surveys (CSUR). 2021 Oct 4; 54(8): 1-36.
7.
Van Der AalstWAdriansyahADe MedeirosAKArcieriFBaierTBlickleTBoseJCVan Den BrandPBrandtjenRBuijsJBurattinA. Process mining manifesto. In Business Process Management Workshops: BPM 2011 International Workshops, Clermont-Ferrand, France, August 29, 2011. Revised Selected Papers, Part I 9. 2012; 169-194. Springer Berlin Heidelberg.
8.
Márquez-ChamorroAEResinasMRuiz-CortésA. Predictive monitoring of business processes: a survey. IEEE Transactions on Services Computing. 2017 Nov 10; 11(6): 962-77.
9.
KratschWManderscheidJRöglingerMSeyfriedJ. Machine learning in business process monitoring: a comparison of deep learning and classical approaches used for outcome prediction. Business and Information Systems Engineering. 2021 Jun; 63: 261-76.
10.
Rama-ManeiroEVidalJCLamaM. Deep learning for predictive business process monitoring: Review and benchmark. IEEE Transactions on Services Computing. 2021 Dec 31; 16(1): 739-56.
11.
BousdekisAKerasiotisAKotsiasSTheodoropoulouGMiaoulisGGhazanfarpourD. Modelling and predictive monitoring of business processes under uncertainty with reinforcement learning. Sensors. 2023 Aug 3; 23(15): 6931.
12.
SavickasTVasilecasO. Belief network discovery from event logs for business process analysis. Computers in Industry. 2018 Sep 1; 100: 258-66.
13.
TeinemaaIDumasMMaggiFMDi FrancescomarinoC. Predictive business process monitoring with structured and unstructured data. In Business Process Management: 14th International Conference, BPM 2016, Rio de Janeiro, Brazil, September 18–22, 2016. Proceedings 14. 2016; 401-417. Springer International Publishing.
14.
KangBKimDKangSH. Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction. Expert Systems with Applications. 2012 Apr 1; 39(5): 6061-8.
15.
PrasidisITheodoropoulosNPBousdekisATheodoropoulouGMiaoulisG. Handling uncertainty in predictive business process monitoring with Bayesian networks. In 2021 12th International Conference on Information, Intelligence, Systems and Applications (IISA). 2021 Jul 12; 1-8. IEEE.
16.
KotsiasSKerasiotisABousdekisATheodoropoulouGMiaoulisG. Predictive and prescriptive business process monitoring with reinforcement learning. InNovel and Intelligent Digital Systems Conferences. 2022 Sep 23; 245-254. Cham: Springer International Publishing.
17.
Di FrancescomarinoCGhidiniC. Predictive process monitoring. In Process Mining Handbook. 2022; 320-346. Cham: Springer International Publishing.
18.
CamargoMDumasMGonzález-RojasO. Learning accurate LSTM models of business processes. InBusiness Process Management: 17th International Conference, BPM 2019, Vienna, Austria, September 1–6, 2019, Proceedings 17. 2019; 286-302. Springer International Publishing.
19.
EvermannJRehseJRFettkeP. Predicting process behaviour using deep learning. Decision Support Systems. 2017 Aug 1; 100: 129-40.
20.
TaxNVerenichILa RosaMDumasM. Predictive business process monitoring with LSTM neural networks. InAdvanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12–16, 2017, Proceedings 29. 2017; 477-492. Springer International Publishing.
21.
NguyenAChatterjeeSWeinzierlSSchwinnLMatznerMEskofierB. Time matters: Time-aware lstms for predictive business process monitoring. In Process Mining Workshops: ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers 2. 2021; 112-123. Springer International Publishing.
22.
HarlMWeinzierlSStierleMMatznerM. Explainable predictive business process monitoring using gated graph neural networks. Journal of Decision Systems. 2020 Aug 18; 29(sup1): 312-27.
23.
MehdiyevNFettkeP. Explainable artificial intelligence for process mining: A general overview and application of a novel local explanation approach for predictive process monitoring. Interpretable artificial intelligence: A perspective of granular computing. 2021; 1-28.
24.
KäppelMJablonskiSSchönigS. Evaluating predictive business process monitoring approaches on small event logs. InInternational Conference on the Quality of Information and Communications Technology. 2021 Aug 25; 167-182. Cham: Springer International Publishing.
25.
ShresthaAMahmoodA. Review of deep learning algorithms and architectures. IEEE Access. 2019 Apr 22; 7: 53040-65.
26.
AbbLPfeifferPFettkePRehseJR. A Discussion on Generalization in Next-Activity Prediction. InInternational Conference on Business Process Management. 2023 Sep 11; 18-30. Cham: Springer Nature Switzerland.
27.
BarbudoRVenturaSRomeroJR. Eight years of AutoML: categorisation, review and trends. Knowledge and Information Systems. 2023 Dec; 65(12): 5097-149.
OlsonRSMooreJH. TPOT: A Tree-Based pipeline optimization tool for automating machine learning. In Workshop on Automatic Machine Learning. 2016 Dec 4; 66-74. PMLR.
30.
RadecicD. Machine Learning Automation with TPOT: Build, validate, and deploy fully automated machine learning models with Python. Packt Publishing Ltd. 2021 May 7.
31.
TruongAWaltersAGoodsittJHinesKBrussCBFarivarR. Towards automated machine learning: Evaluation and comparison of AutoML approaches and tools. In 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI). 2019 Nov 4; 1471-1479. IEEE.
32.
TamaBAComuzziMKoJ. An empirical investigation of different classifiers, encoding, and ensemble schemes for next event prediction using business process event logs. ACM Transactions on Intelligent Systems and Technology (TIST). 2020 Sep 11; 11(6): 1-34.
33.
TaymouriFRosaMLErfaniSBozorgiZDVerenichI. Predictive business process monitoring via generative adversarial nets: the case of next event prediction. InBusiness Process Management: 18th International Conference, BPM 2020, Seville, Spain, September 13–18, 2020, Proceedings 18. 2020; 237-256. Springer International Publishing.
34.
MehdiyevNEvermannJFettkeP. A multi-stage deep learning approach for business process event prediction. In 2017 IEEE 19th Conference on Business Informatics (CBI). 2017 Jul 24; 1: 119-128. IEEE.
