Action-oriented process mining: bridging the gap between insights and actions
TL;DR: In this article , a general framework for action-oriented process mining covering the continuous monitoring of operational processes and the automated execution of management actions is proposed, where actions are generated by analyzing monitoring results in a multi-dimensional way.
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Abstract: Abstract As business environments become more dynamic and complex, it becomes indispensable for organizations to objectively analyze business processes, monitor the existing and potential operational frictions, and take proactive actions to mitigate risks and improve performances. Process mining provides techniques to extract insightful knowledge of business processes from event data collected during the execution of the processes. Besides, various approaches have been suggested to support the real-time (predictive) monitoring of the process-related problems. However, the link between the insights from the continuous monitoring and the concrete management actions for the actual process improvement is missing. Action-oriented process mining aims at connecting the knowledge extracted from event data to actions. In this work, we propose a general framework for action-oriented process mining covering the continuous monitoring of operational processes and the automated execution of management actions. Based on the framework, we suggest a cube-based action engine where actions are generated by analyzing monitoring results in a multi-dimensional way. The framework is implemented as a ProM plug-in and evaluated by conducting experiments on both artificial and real-life information systems.
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Citations
Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs
Gyunam Park
- 01 Jan 2023
TL;DR: In this paper , the authors propose an approach to monitor constraints in object-centric business processes, i.e., multiple case notions (objects) exist, and an event may be associated with multiple objects.
Performance-preserving event log sampling for predictive monitoring
TL;DR: In this article , an instance selection procedure that allows sampling training process instances for prediction models is proposed, which allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.
Optimizing Resource Allocation Based on Predictive Process Monitoring
01 Jan 2023
TL;DR: In this paper , a two-phase method is proposed to improve resource allocation by leveraging predictions, based on design science methodology, where the goal is to allocate appropriate resources to tasks at the proper time.
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A systematic literature review on the application of process mining to Industry 4.0
Katsiaryna Akhramovich,Estefanía Serral,Carlos Cetina +2 more
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Progressing from Process Mining Insights to Process Improvement: Challenges and Recommendations
Vinicius Stein Dani,Henrik Leopold,Jan Martijn E. M. van der Werf,Hajo A. Reijers +3 more
TL;DR: This paper identifies 7 challenges and provides 5 recommendations to bridge the gap between process mining insights and improvements, enabling organizations to effectively utilize process mining for realizing process enhancements through qualitative study and semi-structured interviews.
4
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