7th International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

A Test-bed for the Evaluation of Business Process Prediction Techniques

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2011.247129,
        author={Suraj Pandey and Surya Nepal and Shiping Chen},
        title={A Test-bed for the Evaluation of Business Process Prediction Techniques},
        proceedings={7th International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={IEEE},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={4},
        keywords={business process simulation prediction techniques hidden markov model},
        doi={10.4108/icst.collaboratecom.2011.247129}
    }
    
  • Suraj Pandey
    Surya Nepal
    Shiping Chen
    Year: 2012
    A Test-bed for the Evaluation of Business Process Prediction Techniques
    COLLABORATECOM
    ICST
    DOI: 10.4108/icst.collaboratecom.2011.247129
Suraj Pandey1,*, Surya Nepal1, Shiping Chen1
  • 1: CSIRO
*Contact email: suraj.pandey@csiro.au

Abstract

Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to predict the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete, in order to meet the Service Level Agreements (SLAs) signed with the customers.

In this paper, we focus on the prediction models that could be used for forecasting time to completion of business processes by analysing historical event logs. First, we propose a service oriented architecture that provides a test-bed for carrying out predictions on business processes. Second, we propose a Hidden Markov Model (HMM) based prediction technique that produces a model based on event logs, and compare it against existing prediction models. Finally, we describe an implementation of the system, where we simulate the execution of a business process and obtain predictions using both the proposed and existing prediction techniques.