Who is the Most Diligent Employee? A Discriminate Model for e-Government Service Time Analyzing

This paper discusses the human-related factors contributing to the service time for government services. We build a discriminate model to extract the subjective factor, e.g. diligence, and objective factor from the working behaviors in logs. This model can be trained by maximum likeness estimation (MLE) to get the parameters which denote the factors. To evaluate the accuracy of our model, we conduct an external experiment on a real dataset from the workflow system employed in the government of Hangzhou City in China, which results in 2,367,598 logs from 400 activities and 732 employees in two years. The experiment result not only proves the correctness of our model but also draws some interesting conclusions about the diligence of different people in different ages and genders.