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PM4Py web services: Easy development, integration and deployment of process mining features in any application stack

 
: Berti, A.; Zelst, S.J. van; Aalst, W. van der

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Volltext ()

Depaire, B.:
BPMT 2019. BPM 2019 Dissertation Award, Doctoral Consortium, and Demonstration Track. Online resource : Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019 co-located with 17th International Conference on Business Process Management (BPM 2019), Vienna, Austria, September 1-6, 2019
La Clusaz: CEUR, 2019 (CEUR Workshop Proceedings 2420)
http://ceur-ws.org/Vol-2420/
ISSN: 1613-0073
S.174-178
International Conference on Business Process Management (BPM) <17, 2019, Vienna>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()

Abstract
In recent years, process mining emerged as a set of techniques to analyze process data, supported by different open-source and commercial solutions. Process mining tools aim to discover process models from the data, perform conformance checking, predict the future behavior of the process and/or provide other analyses that enhance the overall process knowledge. Additionally, commercial vendors provide integration with external software solutions, facilitating the external use of their process mining algorithms. This integration is usually established by means of a set of web services that are accessible from an external software stack. In open-source process mining stacks, only a few solutions provide a corresponding web service. However, extensive documentation is often missing and/or tight integration with the front-end of the tool hampers the integration of the services with other software. Therefore, in this paper, a new open-source Python process mining service stack,PM4Py-WS, is presented. The proposed software supports easy integration with any software stack, provides an extensive documentation of the API and a clear separation between the business logic, (graphical) interface and the services. The aim is to increase the integration of process
mining techniques in business intelligence tools.

: http://publica.fraunhofer.de/dokumente/N-630041.html