Abstract
The prime concern for a business organization is to supply quality services to the customers without any delay or interruption so to establish a good reputation among the customer’s and competitors. On-time delivery of a customers order not only builds trust in the business organization but is also cost effective. Therefore, there is a need is to monitor complex business processes though automated systems which should be capable during execution to predict delay in processes so as to provide a better customer experience. This online problem has led us to develop an automated solution using machine learning algorithms so as to predict possible delay in business processes. The core characteristic of the proposed system is the extraction of generic process event log, graphical and sequence features, using the log generated by the process as it executes up to a given point in time where a prediction need to be made (referred to here as cut-off time); in an executing process this would generally be current time. These generic features are then used with Support Vector Machines, Logistic Regression, Naive Bayes and Decision trees to predict the data into on-time or delayed processes. The experimental results are presented based on real business processes evaluated using various metric performance measures such as accuracy, precision, sensitivity, specificity, F-measure and AUC for prediction as to whether the order will complete on-time when it has already been executing for a given period.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aalst, W.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013, 1–37 (2013)
Al-Nasheri, A., et al.: An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification. J. Voice 31(1), 113–e9 (2017)
Alasadi, S.A., Bhaya, W.S.: Review of data preprocessing techniques in data mining. J. Eng. Appl. Sci. 12(16), 4102–4107 (2017)
Aparna, U., Paul, S.: Feature selection and extraction in data mining. In: 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–3. IEEE (2016)
Menard, S.: Applied Logistic Regression Analysis, vol. 106. Sage, Thousand Oaks (2002)
Mesallam, T.A., et al.: Development of the Arabic voice pathology database and its evaluation by using speech features and machine learning algorithms. J. Healthc. Eng. 2017, 1–13 (2017)
Taylor, P.N., Kiss, S.: Rule-mining and clustering in business process analysis. In: Bramer, M., Petridis, M. (eds.) SGAI 2018. LNCS (LNAI), vol. 11311, pp. 237–249. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04191-5_22
Von Rosing, M., Von Scheel, H., Scheer, A.W.: The Complete Business Process Handbook: Body of Knowledge from Process Modeling to BPM, vol. 1. Morgan Kaufmann, Boston (2014)
Wang, L.: Support Vector Machines: Theory and Applications, vol. 177. Springer, Heidelberg (2005). https://doi.org/10.1007/b95439
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2016)
Zhang, H.: The optimality of naive bayes. Am. Assoc. Artif. Intell. 1(2), 3 (2004)
Acknowledgement
This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Khan, N. et al. (2019). A Generic Model for End State Prediction of Business Processes Towards Target Compliance. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-34885-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34884-7
Online ISBN: 978-3-030-34885-4
eBook Packages: Computer ScienceComputer Science (R0)