Discovering more precise process models from event logs by filtering out chaotic activities

Abstract

Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen reallife event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.

Robotic Process Mining: Vision and Challenges

“…A possible solution is to treat noise as chaotic events that can happen anywhere during the process execution. A technique for filtering out such chaotic events is described in Tax et al (2019). However, if noise gravitates towards one particular state or set of states in the task (e.g., towards the start or the end of the task), techniques such as the one mentioned above may not discover it and consequently may not filter it out.…”

Discovering configuration workflows from existing logs using process mining

“…When there is a high human interaction, as in configuration processes, spaghetti and lasagne processes tend to be obtained. The occurrence of infrequent activities or nonrepeated sequence of activities in the analysed log events brings about the necessity to apply frequency-based filtering solutions (Conforti et al 2017) and other based on the discovery of a chaotic set of activities that can be frequent (Tax et al 2019).…”

Application of Clustering Filters in Order to Exclude Irrelevant Instances of the Process Before Using Reinforcement Learning to Optimize Business Processes in the Bank

“…Sample of instances was formed and time of the process instance was calculated for each of the processes. Further, the instances were mapped out manually using features (1) and (2). The next step was to test 15 types of filters for each process: Robust Covariance, one-class SVM, isolation forest, local outlier factor, DBSCAN, Robust Covariance + one-class SVM, Robust Covariance + isolation forest, Robust Covariance + local outlier factor, Robust Covariance + DBSCAN, one-class SVM + isolation forest, oneclass SVM + local outlier factor, one-class SVM + DBSCAN, isolation forest + local outlier factor, isolation forest + DBSCAN, local outlier factor + DBSCAN.…”