Large service companies run complex customer service processes to provide communication services to their customers. The flawless execution of these processes is essential because customer service is an important differentiator. They must also be able to predict if processes will complete successfully or run into exceptions in order to intervene at the right time, preempt problems and maintain customer service. Business process data are sequential in nature and can be very diverse. Thus, there is a need for an efficient sequential forecasting methodology that can cope with this diversity. This paper proposes two approaches, a sequential k nearest neighbour and an extension of Markov models both with an added component based on sequence alignment. The proposed approaches exploit temporal categorical features of the data to predict the process next steps using higher order Markov models and the process outcomes using sequence alignment technique. The diversity aspect of the data is also added by considering subsets of similar process sequences based on k nearest neighbours. We have shown, via a set of experiments, that our sequential k nearest neighbour offers better results when compared with the original ones; our extension Markov model outperforms random guess, Markov models and hidden Markov models.

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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.