Sequential Clustering for Event Sequences and Its Impact on Next Process Step Prediction

Abstract

Next step prediction is an important problem in process analytics and it can be used in process monitoring to preempt failure in business processes. We are using logfiles from a workflow system that record the sequential execution of business processes. Each process execution results in a timestamped event. The main issue of analysing such event sequences is that they can be very diverse. Models that can effectively handle diverse sequences without losing the sequential nature of the data are desired. We propose an approach which clusters event sequences. Each cluster consists of similar sequences and the challenge is to identify a similarity measure that can cope with the sequential nature of the data. After clustering we build individual predictive models for each group. This strategy addresses both the sequential and diverse characteristics of our data. We first employ K-means and extent it into a categorical-sequential clustering algorithm by combining it with sequential alignment. Finally, we treat each resulting cluster by building individual Markov models of different orders, expecting that the representative characteristics of each cluster are captured. © Springer International Publishing Switzerland 2014.