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.
How to install:
Please follow the detailed instructions available at this link: SUMMER_SCHOOL_PPM_NIRDIZATI.
The space required to complete the installation is ~3GB.
Example datasets: Sepsis log (included in the link above.)
Contact person providing support during the summer school:
Williams Rizzi (email@example.com)
Providing support on the following days: 7-8 July