Automated Analysis of the Business Process Using Process Mining

Within present environment of increasingly aggressive competition, optimization of business processes is becoming a key factor that determines successful development of many firms in different industries. Identification of abnormal behavior and bottlenecks in the business process and evaluation of its future behavior are regarded as one of most important competitive advantages, since it helps to reduce costs and protects acquired market segments throught high rates of improving business process. The main goal of the project is to build a software tool with technical functionality: process visualization, calculation and visualization of the KPI process, prediction of the next event, automatic allocation of anomalous chains. In this graduate work we investigate various types of process vectorization and classification algorithms from simple linear models to neural networks to solve the problem of predicting the next chain event. Keywords - analysis of business processes, data mining, machine learning, allocation of anomalous business process cases, visualization of the business process, process mining.