A semi-automatic approach for workflow staff assignment

Staff assignment is of great importance for workflow management systems. In many workflow applications, staff assignment is still performed manually. In this paper, we present a semi-automatic approach intended to reduce the number of manual staff assignment. Our approach applies a machine learning algorithm to the workflow event log to learn various kinds of activities that each actor undertakes. When staff assignment is needed, the classifiers generated by the machine learning technique suggest a suitable actor to undertake the specified activities. With experiments on three enterprises, our approach achieved a fairly accurate recommendation. (c) 2008 Elsevier B.V. All rights reserved.