Nirdizati: an Advanced Predictive Process Monitoring Toolkit
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 56 references that are in the map. First posted on arXiv 18 Oct 2022.
Builds on
14 of 56 references.
References · 14
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- 2016Predictive Business Process Monitoring with LSTM Neural Networkscited by 103
- 2020Deep Learning for Predictive Business Process Monitoring: Review and Benchmarkcited by 45
- 2018Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoringcited by 43
- 2013Predictive Monitoring of Business Processescited by 37
- 2017Outcome-Oriented Predictive Process Monitoring: Review and Benchmarkcited by 37
- 2019Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Sciencecited by 32
- 2018Predictive Process Monitoring Methods: Which One Suits Me Best?cited by 27
- 2015Clustering-Based Predictive Process Monitoringcited by 24
- 2016Time and Activity Sequence Prediction of Business Process Instancescited by 24
- 2021Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoringcited by 16
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