2019Unpublished venueRequires access

Overlapping Analytic Stages in Online Process Mining

Gabriel Marques Tavares, Paolo Ceravolo, Victor G. Turrisi da Costa, Ernesto Damiani, Sylvio Barbon

Open publisher page 30 citations

Abstract

Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously?: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.

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What this paper is about

Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously?: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.

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OpenAlex reports 30 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously?: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.

Key concepts: Process mining, Conformance checking, Computer science, Business process discovery, Process (computing), TRACE (psycholinguistics), Data mining, Cluster analysis

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