Nirdizati: an Advanced Predictive Process Monitoring Toolkit

TL;DR

Nirdizati is a comprehensive toolkit designed to support researchers and practitioners in building, comparing, and analyzing predictive models for business process monitoring, enhancing the process of selecting suitable techniques.

Contribution

The paper introduces Nirdizati, a modular and scalable toolkit that integrates multiple state-of-the-art predictive process monitoring approaches for improved analysis and model explanation.

Findings

Supports diverse predictive techniques

Enhances model comparison and analysis

Improves modularity and scalability of the toolkit

Abstract

Predictive Process Monitoring is 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. The recent stream of publications in this field shows the need for tools able to support researchers and users in analyzing, comparing and selecting the techniques that are the most suitable for them. Nirdizati is a dedicated tool for supporting users in building, comparing, analyzing, and explaining 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 the current version of Nirdizati, together with its architecture which has been developed to improve its modularity and scalability. The features of Nirdizati enrich its capability to support researchers and practitioners within the entire pipeline for constructing reliable Predictive Process Monitoring models.