Case identification approach for unlabeled event logs
Process mining aims to extract useful information from event logs,so as to discover,monitor and improve the actual business process.Most process mining technologies rely on standardized event logs,that is,each event in the event log corresponds to a case.However,the existing mining technology cannot deal with the unlabeled event log.To solve this problem,a case identification approach for unlabeled event log was proposed.Specifically,the de-gree of dependence between activities from the unlabeled event log was mined first according to the association rules,so as to mine the dependency between activities;according to the dependency relationship between activities,the possible activity relationships that was concurrency relation,exclusion relation and loop relation among activities was mined;finally a case identification algorithm was proposed to recognize the unlabeled event log and get the la-beled event log.The proposed approach had been implemented in the open source platform ProM tool.Based on sim-ulated log datasets and real log datasets,the effectiveness of this approach was verified.Through quantitative com-parison with the current best approach in the field,the advantages of our approach were further verified.
process miningPetri netunlabeled event logcase identification
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NSTL
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