3 research outputs found

    A hybrid model for business process event and outcome prediction

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    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

    Hybrid intelligent approaches for business process sequential analysis.

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    The quality of customer services is an important differentiator for service oriented com- panies like telecommunication providers. In order to deliver good customer service, the underlying processes within the operations of a company have to run smoothly and must be well controlled. It is of great importance to be able to predict if processes are likely to fail and to be aware of developing problems as early as possible. A failure in a customer service process typically results in a negative experience for a customer and companies are keen to avoid this from happening. Process performance prediction allows companies to pro-actively adapt with process execution in order to prevent process problems from affect- ing their customers. Process analytics is often compounded by a number of factors. Very often processes are only poorly documented because they have evolved over time together with the legacy IT systems that were used to implement them. The workflow data that is collected during process execution is high dimensional and can contain complex attributes and very diverse values. Since workflow data is sequential in nature, there are a number of data mining methods such as sequential pattern mining and probabilistic models that can be useful for predicting process transitions or process outcomes. None of these techniques alone can adequately cope with workflow data. The purpose of this thesis is to contribute a combination of methods that can analyse data from business process in execution in order to predict severe process incidents. In order to best exploit the sequential nature of the data we have used a number of sequential data mining approaches coupled with sequence alignment and a strategy for dealing with similar sequences. The methods have been applied to real process data from a large telecommunication provider and we have conducted a number of experiments demonstrating how to predict process steps and process outcomes. Finally, we show that the performance of the proposed models can be significantly improved if they are applied to individual clusters of workflow data rather than the complete set of process data

    Grundlagen der Informationswissenschaft

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