Le, M. P., Gabrys, B. and Nauck, D., 2014. A hybrid model for business process event and outcome prediction. Expert Systems.
Full text available as:
Le_Gabrys_Nauck_Expert_Systems_2014.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.
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.
|Uncontrolled Keywords:||Artificial intelligence ; Method ; System|
|Group:||Faculty of Science and Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||02 Oct 2015 13:58|
|Last Modified:||07 Oct 2015 07:39|
Downloads per month over past year
|Repository Staff Only -|