Data Preprocessing for Goal-Oriented Process Discovery

Abstract

Goal-oriented process enhancement and discovery (GoPED) was recently proposed to take advantage of goal modeling capabilities in process mining activities. Conventional process mining aims to discover underlying process models from historical, crowdsourced event logs in an activity-oriented fashion. GoPED, however, infers goal-aligned process models from the event logs enhanced with some goal-related attributes. GoPED selects the historical behaviors that have yielded sufficient levels of satisfaction for (often conflicting) goals of different stakeholders. There are three algorithms available to select the subset of event logs from three different perspectives. The main input of all three algorithms is a version of the event log (EnhancedLog) that is (1) structured as a table showing each case and its trace in one row, (2) with rows enhanced with satisfaction levels of different goals. Therefore, typical event logs are not ready to be fed as-is to GoPED algorithms. This paper proposes a scheme for manipulating original event logs and turn them into EnhancedLog. Two tools were also developed and tested for this scheme: TraceMaker, to structure the log as explained above, and EnhancedLogMaker, to compute satisfaction levels of goals for all cases in the structured log.