Process mining is an established approach for analyzing and modeling complex business processes. In this paper we showcase ProMetheuS, a flexible and scalable suite for process mining natively designed for industrial applications. Moving from the experience of the ProM framework, the state-of-art process mining tool, ProMetheuS introduces three innovative designing elements. Firstly, ProMetheuS defines the concept of flow of mining, which is aimed at supporting the design of complex mining applications, where various mining tasks can be combined and automatically orchestrated at run-time. Secondly, ProMetheuS exports a rich set of facilities to help developers in building interactive applications providing on-the-fly feedback during analysis. Finally, behind the scenes, a powerful stream-based log-handling subsystem ensures scalability in data-intensive applications.

ProMetheuS: A Suite for Process Mining Applications / Ghionna, L; Granata, L; Greco, Gianluigi; Guarascio, M.. - 855 CEUR(2012), pp. 58-65. ((Intervento presentato al convegno CAiSE'12 Forum tenutosi a Gdansk, Poland nel June 28.

ProMetheuS: A Suite for Process Mining Applications

GRECO, Gianluigi;
2012

Abstract

Process mining is an established approach for analyzing and modeling complex business processes. In this paper we showcase ProMetheuS, a flexible and scalable suite for process mining natively designed for industrial applications. Moving from the experience of the ProM framework, the state-of-art process mining tool, ProMetheuS introduces three innovative designing elements. Firstly, ProMetheuS defines the concept of flow of mining, which is aimed at supporting the design of complex mining applications, where various mining tasks can be combined and automatically orchestrated at run-time. Secondly, ProMetheuS exports a rich set of facilities to help developers in building interactive applications providing on-the-fly feedback during analysis. Finally, behind the scenes, a powerful stream-based log-handling subsystem ensures scalability in data-intensive applications.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/187333
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