Decomposition using Refined Process Structure Tree (RPST) and control flow complexity metrics

Abstract

Process mining is a technique that aims to gain knowledge of the event log. The amount of data in the event log is very influential in the Process mining, because it contains millions of activities that shape the behavior of a company. The three main capabilities possessed by mining process is a discovery, conformance, and enhancement. This paper, we present an approach to decompose business processes using Refine Process Structure Tree (RPST). By breaking down a whole into sub models Business Processes (fragments) to the smallest part (atomic) can facilitate the analysis process and can easily be rebuilt. To measure the level of complexity in the model fragment and atomic models we use complexity Control flow metrics. Control flow complexity metrics have two main approaches that are count based measurement and execution path based measurement path. Count based measurement used to describe a static character, while an execution path based measurement used to describe the dynamic character of each model fragment or atomic models (bond fragment).