A Deep Learning Approach to Business Process Mining
Hanga, Khadijah (2023) A Deep Learning Approach to Business Process Mining. Doctoral thesis, Birmingham City University.
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Khadijah Hanga PhD Thesis published_Final version_Submitted Oct 2022_Final Award Feb 2023.pdf - Accepted Version Download (3MB) |
Abstract
Competing and evolving markets force organisations to continuously monitor, evaluate, and optimise their business processes. To do the task at scale, organisations often turn to automatic mining of process execution logs constantly generated by various information systems. Many open-source and commercial tools have been developed in recent years to help organisations perform various process mining tasks using process execution logs (often called event logs), such as process discovery, conformance checking, and detecting drifts in processes. Compared to traditional process mining techniques such as Petri nets and Business Process Model and Notation (BPMN), deep learning methods such as Recurrent Neural Networks and Long Short-Term Memory (LSTM) in particular have proven to achieve better performance in terms of accuracy and generalising ability when predicting sequences of activities performed as part of business processes based on event logs. However, unlike traditional network-based process mining techniques that can be used to visually present all activity sequences of the discovered business process, existing deep learning-based methods for process mining lack a mechanism explaining how the activity sequence predictions are made. To address this limitation, this thesis proposes an extensible process mining solution that combines the benefits of interpretable graph-based methods and more accurate but implicit deep learning methods. The main contributions of this research are: (i) building an LSTM model for predicting business process activity sequences from event logs that outperforms existing state-of-the-art deep learning solutions; (ii) proposing a graph-based approach to explaining the decision-making process of the LSTM model when predicting business process activity sequences; and (iii) developing methods for detecting and localising sudden concept drift in event logs (i.e., offline) and event streams (i.e., online) using deep learning and graph-based approaches. The proposed methods have been extensively evaluated by conducting experiments using real-life and artificial event logs and have been demonstrated to outperform existing state-of-the-art solutions in many cases.
| Item Type: | Thesis (Doctoral) | ||||||
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| Uncontrolled Keywords: | Process Mining, Business Process Management, Deep Learning, Long Short-Term Memory, Concept Drift Detection, Concept Drift Localisation | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-07 - business computing | ||||||
| Divisions: | Doctoral Research College > Doctoral Theses Collection Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology |
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| Depositing User: | Jaycie Carter | ||||||
| Date Deposited: | 27 Mar 2023 14:35 | ||||||
| Last Modified: | 27 Mar 2023 14:35 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14286 |
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