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

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... 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.