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International audienceProcess mining has an important place in business process (BP) analysis. It aims to analyze process events in order to discover the related BP model. However, these techniques are only based on process events and sometimes on business data, leaving aside a large set of data, namely the BP execution context. Existing studies have shown the benefits of considering the context in the BP analysis but they only suggest manual techniques to bind a BP with its context, which is not scalable and time consuming in real deployment environment. To address this issue, we propose a semi-automatic BP contextualization solution which takes into account the BP execution context in the BP analysis time. It uses semantic techniques to perform a matching of the BP model with contextual data and with business data, and then it obtains the value of these data during the BP execution. In this paper, we present a tool that implements this solution in order to enhance current analysis techniques which facilitates the monitoring and enables deep BP analysis. The proposed tool is validated by its application to a real life process, a "palettization" process

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