Nirdizati: A Web-Based Tool for Predictive Process Monitoring

This paper introduces Nirdizati: A web-based application for generating predictions about running cases of a business process. Nirdizati is a configurable full-stack web application that supports users in selecting and tuning prediction methods from a list of implemented algorithms and enables the continuous prediction of various performance indicators at runtime. The tool can be used to predict the outcome, the next events, the remaining time, or the overall workload per day of each case of a process. For example, in a lead-To-order process, Nirdizati can predict which customer leads will convert to purchase orders and when. In a claim handling process, it can predict if a claim decision will be made on time or late. The predictions, as well as real-Time summary statistics about the process executions, are presented in a dashboard that offers multiple visualization options. Based on these predictions, process participants can act proactively to resolve or mitigate potential process pe...

This paper introduces Nirdizati: A web-based application for generating predictions about running cases of a business process. Nirdizati is a configurable full-stack web application that supports users in selecting and tuning prediction methods from a list of implemented algorithms and enables the continuous prediction of various performance indicators at runtime. The tool can be used to predict the outcome, the next events, the remaining time, or the overall workload per day of each case of a process. For example, in a lead-to-order process, Nirdizati can predict which customer leads will convert to purchase orders and when. In a claim handling process, it can predict if a claim decision will be made on time or late. The predictions, as well as real-time summary statistics about the process executions, are presented in a dashboard that o↵ers multiple visualization options. Based on these predictions, process participants can act proactively to resolve or mitigate potential process performance violations. The target audience of this demonstration includes process mining researchers as well as practitioners interested in exploring the potential of predictive process monitoring.