A Business-Data-Oriented Workflow Mining Algorithm and Its Application

Abstract

The mining of workflow process aims at finding valuable objective information from log data. It leads useful implications for new business processes and analysis. Unfortunately most of business process data is incomplete and noisy which brings deficiencies for describing and mining workflow. The existing algorithms ignore time-based parameters, which is important for processing the incomplete workflow data. In this paper, we define the parameters of single transaction frequency and time intervals. Then we propose a business-data-oriented workflow excavation algorithm (termed as E-α-algorithm), which improves the exploration of differences between the actual business processes by removing noisy data efficiently. With this new algorithm, we aim to optimize the key business process model and build future intelligent workflow system to assist decision-making and process mechanism optimization.