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To browse Academia. Ahmad Aminu. Petter Nielsen , Julian Bass. Kingsley Okoye PhD. Discovery of worthwhile process models and effective data representation must be performed with due regard to the transformation that needs to be achieved, and the available data processing tools both at the pre-modelling and post-modelling stages. Indeed, such transformations should be aimed at turning data into real value.
Presently, the field of process mining has been proven to provide valuable techniques that are used to improve real time processes by extracting knowledge from event logs readily available in many organisations information systems. Practically, there are two main drivers for such growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about history of processes as they happen in reality. On the other hand, there is need to improve and support business processes in a competitive and rapidly changing environment.
Process mining means extracting valuable, process-related information from event logs about any real time process. Besides, process discovery has been lately seen as the most important and most visible intellectual challenge related to process mining. The arrangement involves automatic construction of process models from event log about any domain process, and describes causal dependencies between the various activities that are performed within the process base i. In principle, one can use process discovery to obtain process models that describes reality.
In view of that, the work in this paper presents a Fuzzy-BPMN mining approach that uses a training event log representing 10 different realtime business process executions to provide a method for discovery of useful process models, and then cross-validating the derived models with a set of test event logs in order to measure the performance of the employed discovery method. Our aim is on carrying out a classification task to determine the traces, ie. Thus, we focus on providing a model which is as good in balancing between overfitting and underfitting as it is able to correctly classify the traces that can be replayed allowed or non-replayable disallowed based on the analysis of the event logs and the discovered processs models.
