Prediction of Business Process Outcome based on Historical Log

With the development of data mining and machine learning, we can get much useful information from historical data. For a business process system, it maintains large amount of process execution data, especially records of events corresponding to the execution of activities, which can also be called event log. Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions and recommendation about current running cases. This paper proposes an improved approach for process outcome prediction and next activity recommendation. It estimates the accuracy that a given goal will be fulfilled upon completion of a current running process case through three different methods. Each method includes both clustering phase and classification phase. However, different levels of historical data (business level and control flow level) in event log are used, and the size of data and number of features also differs. We show our improved approach to deal with historical log, encode each feature vector, train predictive model and how to use trained models for predicting the outcome of current case and recommending the next event. Finally, through a series of experiment, we compare three different method and existing approach.