Abstract:

Process mining is a technique for extracting knowledge from event logs recorded by an information system. In the process discovery phase of process mining, a process model is constructed to represent the business processes systematically and to give a general opinion about the progressive of processes in the event log. The constructed process model can be very complex as a result of structured and unstructured processes recorded in real life. Previous studies proposed different approaches to filter or eliminate some processes from the model to simplify it by implementing some statistical or mathematical formulas rather than user interactions. The main objective of this study is to develop an algorithm that is capable of working on large volume of event logs and handling the execution records of running process instances to analyze the execution of processes. The other significant principle is to provide an interactive method to ensure the decisions that will be taken to improve the execution of processes by verifying in a simulation environment before being put into practice. This study proposes a novel algorithm, named interactive process miner, to create a process model based on event logs and a new approach that contains three different features, including activity deletion, aggregation, and addition operations on the existing process model. The experimental results show a fundamental improvement in performance compared to the existing algorithms. As a result of this study, users will have an opportunity to analyze a large volume of event logs in a short time with low memory usage and to modify the created process model to observe the impact of improvement changes in a simulation environment before applying any changes to a system in real life.

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