Enabling Process Mining in Aircraft Manufactures: Extracting Event Logs and Discovering Processes from Complex Data

Abstract

Process mining is employed by organizations to completely understand and improve their processes and to detect possible deviations from expected behavior. Process discovery uses event logs as input data, which describe the times of the actions that occur the traces. Currently, Internet-of-Things environments generate massive distributed and not always structured data, which brings about new complex scenarios since data must first be transformed in order to be handled by process min ing tools. This paper shows the success case of application of a solution that permits the transformation of complex semi-structured data of an assembly-aircraft process in order to create event logs that can be man aged by the process mining paradigm. A Domain-Specific Language and a prototype have been implemented to facilitate the extraction of data into the unified traces of an event log. The implementation performed has been applied within a project in the aeronautic industry, and promis ing results have been obtained of the log extraction for the discovery of processes and the resulting improvement of the assembly-aircraft process.