Nirdizati is a dedicated tool for Predictive Process Monitoring, a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. Nirdizati is a web application supporting users in building, comparing, and analyzing predictive models that can then be used to perform predictions on the future of an ongoing case. By providing a rich set of different state-of-the-art approaches, Nirdizati offers BPM researchers and practitioners a useful and flexible instrument for investigating and comparing Predictive Process Monitoring techniques. In this paper, we present a Nirdizati version with a redesigned backend, which improves its modularity and scalability, and with new features, which further enrich its capability to support researchers and practitioners to deal with different monitoring tasks.

Nirdizati 2.0: New Features and Redesigned Backend

Williams Rizzi;Luca Simonetto;Chiara Di Francescomarino;Chiara Ghidini;
2019-01-01

Abstract

Nirdizati is a dedicated tool for Predictive Process Monitoring, a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. Nirdizati is a web application supporting users in building, comparing, and analyzing predictive models that can then be used to perform predictions on the future of an ongoing case. By providing a rich set of different state-of-the-art approaches, Nirdizati offers BPM researchers and practitioners a useful and flexible instrument for investigating and comparing Predictive Process Monitoring techniques. In this paper, we present a Nirdizati version with a redesigned backend, which improves its modularity and scalability, and with new features, which further enrich its capability to support researchers and practitioners to deal with different monitoring tasks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/320712
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