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Mining Intentional Process Models

Abstract : So far, process mining techniques suggested to model processes in terms of tasks that occur during the enactment of a process. However, research on process modeling has illustrated that many issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. This thesis presents a novel approach of process mining, called Map Miner Method (MMM). This method is designed to automate the construction of intentional process models from traces. MMM uses Hidden Markov Models to model the relationship between users' activities and the strategies (i.e., the different ways to fulfill the intentions). The method also includes two specific algorithms developed to infer users' intentions and construct intentional process model (Map), respectively. MMM can construct Map process models with different levels of granularity (pseudo-Map and Map process models) with respect to the Map metamodel formalism. The entire proposed method was applied and validated on practical traces in a large-scale experiment, on event logs of developers of Eclipse UDC (Usage Data Collector). The resulting Map process models provide a precious understanding of the processes followed by the developers, and also provide feedback on the effectiveness and demonstrate scalability of MMM in terms of traces. Map Miner tool has been developed to enable practicing the proposed approach. This permits users to obtain the pseudo-Map and Map process model out of traces.
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Contributor : Ghazaleh Khodabandelou Connect in order to contact the contributor
Submitted on : Friday, June 20, 2014 - 1:41:10 PM
Last modification on : Tuesday, January 19, 2021 - 11:08:39 AM
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  • HAL Id : tel-01010756, version 1



Ghazaleh Khodabandelou. Mining Intentional Process Models. Machine Learning [stat.ML]. Université Panthéon-Sorbonne - Paris I, 2014. English. ⟨tel-01010756⟩



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