=Paper=
{{Paper
|id=Vol-3758/paper-20
|storemode=property
|title=Nirdizati Light: A Modular Framework for Explainable Predictive Process Monitoring
|pdfUrl=https://ceur-ws.org/Vol-3758/paper-20.pdf
|volume=Vol-3758
|authors=Andrei Buliga,Riccardo Graziosi,Chiara Di Francescomarino,Chiara Ghidini,Fabrizio Maria Maggi,Williams Rizzi,Massimiliano Ronzani
|dblpUrl=https://dblp.org/rec/conf/bpm/BuligaGFGMRR24
}}
==Nirdizati Light: A Modular Framework for Explainable Predictive Process Monitoring==
https://ceur-ws.org/Vol-3758/paper-20.pdf
Nirdizati Light: A Modular Framework for
Explainable Predictive Process Monitoring
Andrei Buliga1,2,∗,† , Riccardo Graziosi2,† , Chiara Di Francescomarino3 ,
Chiara Ghidini1 , Fabrizio Maria Maggi1 , Williams Rizzi4 and Massimiliano Ronzani2
1
Free University of Bozen-Bolzano Bolzano, Italy
2
Fondazione Bruno Kessler, Trento, Italy
3
University of Trento, Trento, Italy
3
Nexoya, Zurich, Switzerland
Abstract
Nirdizati Light is an innovative Python package designed for Explainable Predictive Process Monitoring
(XPPM). It addresses the need for a modular, flexible tool to compare predictive models, and generate
explanations for the predictions made by the predictive models. By integrating consolidated frameworks
libraries for process mining, machine learning, and explainable AI, it offers a comprehensive approach to
predictive model construction and explanation generation. This paper discusses the tool’s key features,
and its significance in the BPM community.
Keywords
predictive process monitoring, machine learning, explainable AI
1. Introduction
Nirdizati Light is an innovative Python-based (Explainable) Predictive Process Monitoring
(PPM) [1] tool offering a wide array of approaches and providing researchers and practitioners
with a highly modular solution for the instantiation, comparison, analysis, selection, and
explanation of predictive models for different types of prediction tasks. Existing tools like
Nirdizati [2] have significantly contributed to this field by offering robust capabilities for building,
analysing, and comparing predictive models, offering also a glimpse into the application of
Explainable AI techniques in PPM. However, Nirdizati faces notable limitations that hinder
experimental flexibility, as it is primarily tied to a user interface, restricting customization and
the seamless integration of new techniques. Its fixed set of models and hardcoded workflows
limits adaptability and scalability, posing challenges for researchers and practitioners who wish
to innovate or tailor the tool to specific needs.
Proceedings of the Best BPM Dissertation Award, Doctoral Consortium, and Demonstrations & Resources Forum co-located
with 22nd International Conference on Business Process Management (BPM 2024), Krakow, Poland, September 1st to 6th,
2024.
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open abuliga@fbk.eu (A. Buliga); rgraziosi@fbk.eu (R. Graziosi); c.difrancescomarino@unitn.it (C. Di
Francescomarino); chiara.ghidini@unibz.it (C. Ghidini); maggi@inf.unibz.it (F. M. Maggi); mronzani@fbk.eu
(M. Ronzani)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: Diagram showing the overall pipeline for Nirdizati Light.
In response to these constraints, in this demo paper we introduce a modular and extensible
Python-based version of Nirdizati, by offering more encoding techniques, newer state-of-the-
art predictive models, with a particular focus on novel XAI techniques adapted to the PPM
domain. With Nirdizati Light, users can explore a diverse set of trace encodings, predictive tasks,
predictive models, and explanations, enhancing their ability to make data-driven decisions.
2. Nirdizati Light innovations for (X)PPM
Predictive Process Monitoring (PPM) is crucial for operational optimisation and informed
decision-making. Fig. 1 shows a general pipeline employed for PPM. However, existing PPM
methods often lack transparency and fail to incorporate domain-specific knowledge, limiting
their effectiveness. The adoption of Deep Learning models in Predictive Process Monitoring
(PPM) has synchronously brought upon the adoption of explanatory techniques intending to
provide explanations for different prediction tasks. This has lead to the creation of a novel
subfield, named Explainable Predictive Process Monitoring (XPPM) [3].
Nirdizati Light is a modular Python package that supports PPM by providing a comprehensive
suite of functionalities for Explainable Predictive Process Monitoring (XPPM). Designed with
flexibility at its core, Nirdizati Light 1 allows users to seamlessly import event logs, experiment
with a range of encoding techniques, and train various predictive models. It integrates popular
libraries such as pm4py [4] for event log handling, scikit-learn 2 and PyTorch 3 for model
training, and hyperopt 4 for hyperparameter optimisation. This integration facilitates a cohesive
environment where users can conduct all stages of event log analysis within a single platform.
A standout feature of Nirdizati Light is its modularity, enabling users to effortlessly swap
1
The tool is available at the following repository link https://github.com/rgraziosi-fbk/nirdizati-light, while the
video demonstration for the tool can be found at https://tinyurl.com/bdhbwwhz
2
https://scikit-learn.org/
3
https://pytorch.org/
4
https://hyperopt.github.io/hyperopt/
components like encodings, models, and explainable AI (XAI) methods. This flexibility supports
a dynamic experimentation process without being confined to a rigid interface. The tool supports
a diverse array of predictive tasks, including outcome prediction, next activity prediction,
remaining time prediction, and trace duration prediction. This breadth of capabilities allows it
to cater to a wide range of use cases and data characteristics, independently on whether the task
involves classification or regression. Fig. 1 also highlights the main functionalities of Nirdizati
light. We present each of the submodules of the framework below.
Event Log labeling. The Prediction task definition module enables the automatic labeling
of logs with various predictions, including categorical outcomes, numeric values, and next
activities. For categorical outcomes, it allows for multiclass labels from categorical attributes
and next activities, as well as binary labels for outcome predictions. For numerical outcomes, it
supports numeric labels derived from numeric attributes and trace duration.
Trace Encoder/Decoder. The Encoding selection module processes labelled event logs
and converts them into a DataFrame suitable for machine learning. This transformation occurs
through three steps: (i) Encoding information extraction: This step extracts critical attributes
from the event log, such as control-flow (activity names), data flow (trace and event attributes),
and resource-flow (resource-related attributes). This mapping identifies the relevant information
for encoding; (ii) Feature encoding: Using the extracted information, this step determines the
feature set that will represent each trace in the DataFrame; (iii) Data encoding: Finally, the
feature set is transformed into a DataFrame. This includes operations like one-hot encoding
of categorical features and normalization of numeric attributes, ensuring the data is ready for
training predictive models. For this we make use of the scikit-learn library.
Predictive Model Selection + Optimisation. The Model(s) selection module allows users
to specify and instantiate predictive models. It supports both classification and regression
algorithms. The modular design of Nirdizati Light permits the integration and expansion of
additional predictive algorithms, enhancing its adaptability to different requirements. For the
predictive models, Nirdizati Light uses popular Machine Learning/Deep Learning libraries such
as scikit-learn and PyTorch to instantiate the predictive models within the framework.
Hyperparameter optimisation. This module enhances model performance by automating
the tuning of hyperparameters using the hyperopt library. This module receives the training
DataFrame and an instantiated predictive model, then explores multiple hyperparameter con-
figurations to maximize a specified quality metric. This process, although computationally
intensive, significantly improves the accuracy and effectiveness of the predictive models.
Predictive Model Comparison. The Model evaluation module provides a comprehensive
assessment of predictive models based on two primary classes of metrics: (i) Time metrics:
Evaluate the speed at which the predictive model trains, updates, and generates predictions; (ii)
Accuracy metrics: Assess the model’s predictive performance on the test set.
This module facilitates detailed comparisons between different models, offering insights
into their performance across various configurations and datasets. Nirdizati Light supports a
streamlined workflow from data preprocessing to model evaluation, making it an invaluable
tool for researchers and practitioners in the BPM community.
Explainability. Nirdizati Light also excels in generating actionable insights through state-
of-the-art XAI methods, incorporating advanced tools such as SHAP (SHapley Additive ex-
Planations) [5], LiME [6], and DiCE (Diverse Counterfactual Explanations) [7] through the
Explanation method selection module. These methods provide deep, interpretative insights
into model predictions, enhancing their transparency and utility. Furthermore, the tool em-
phasizes knowledge-aware explainability, leveraging domain-specific knowledge to produce
explanations that are not only accurate but also meaningful and easy to understand. Further-
more, we also include a selection of state-of-the-art XPPM techniques [8, 9], which leverage
domain-specific knowledge, either through the form of temporal constraints (LTLf and Declare),
or by providing explanations in terms of process patterns 5 . These adapted techniques focus on
both providing the reasons for the prediction made by the model (so-called factual explanations)
and showing the required changes to the input to achieve an alternative outcome (also known
as counterfactual explanations). By integrating these advanced features and methodologies,
Nirdizati Light empowers process analysts and data scientists to unlock profound insights from
event logs and make well-informed decisions. Its ability to support flexible experimentation
and deliver interpretive, domain-specific explanations marks a significant advancement in the
XPPM domain, providing a robust and intuitive platform for comprehensive data analysis.
3. Concluding Remarks
This paper introduced Nirdizati Light, a significant advancement in the realm of Explainable
Predictive Process Monitoring (XPPM), addressing the limitations of existing tools like Nirdizati
by offering a modular, flexible, and powerful Python package that facilitates the construction
and comparison of different predictive models and trace encodings for a given event log.
Its architecture supports easy integration and comparison of various encoding techniques,
predictive models, and state-of-the-art explainability methods, while its modularity allows users
to experiment with and adopt the latest advancements in predictive process monitoring, tailoring
solutions to specific use cases. This flexibility is crucial in the PPM domain, where researchers
and practitioners need adaptable tools for a wide range of scenarios and data characteristics.
We assess the current Technology Readiness Level of Nirdizati Light to be a 4, reflecting
its well-defined software structure, its versatility and robustness demonstrated through past
applications in various domains [12, 13, 8, 9]. With its flexible framework and feature set, the tool
offers researchers and practitioners a tool to enhance their understanding of predictive process
monitoring techniques, and easily extend the framework with additional custom methods.
5
See [10] for more details on Declare and LTLf, and [11] for more details on process patterns.
4. Acknowledgments
This work was partially supported by the Italian (MUR) under PRIN project PINPOINT Prot.
2020FNEB27, CUP H23C22000280006 and H45E21000210001, the support is greatly appreciated.
References
[1] C. Di Francescomarino, C. Ghidini, Predictive process monitoring, in: Process Mining
Handbook, volume 448 of LNBIP, Springer, 2022, pp. 320–346.
[2] W. Rizzi, L. Simonetto, C. Di Francescomarino, C. Ghidini, T. Kasekamp, F. M. Maggi,
Nirdizati 2.0: New features and redesigned backend, in: Proceedings of the Dissertation
Award, Doctoral Consortium, and Demonstration Track at BPM 2019, volume 2420 of
CEUR Workshop Proceedings, CEUR-WS.org, 2019, pp. 154–158.
[3] M. Stierle, J. Brunk, S. Weinzierl, S. Zilker, M. Matzner, J. Becker, Bringing light into
the darkness-a systematic literature review on explainable predictive business process
monitoring techniques, ECIS 2021 Research-in-Progress Papers 8 (2021).
[4] A. Berti, S. van Zelst, D. Schuster, Pm4py: A process mining library for python, Software
Impacts 17 (2023) 100556.
[5] S. M. Lundberg, S. Lee, A unified approach to interpreting model predictions, in: Ad-
vances in Neural Information Processing Systems 30: Annual Conf. on Neural Information
Processing Systems 2017„ 2017, pp. 4765–4774.
[6] M. T. Ribeiro, S. Singh, C. Guestrin, “Why should i trust you?” Explaining the predictions
of any classifier, in: Proceedings of the 22nd ACM SIGKDD international conference on
knowledge discovery and data mining, 2016, pp. 1135–1144.
[7] R. K. Mothilal, A. Sharma, C. Tan, Explaining machine learning classifiers through di-
verse counterfactual explanations, in: Proceedings of the 2020 Conference on Fairness,
Accountability, and Transparency, 2020, pp. 607–617.
[8] A. Buliga, C. Di Francescomarino, C. Ghidini, F. M. Maggi, Counterfactuals and ways to
build them: Evaluating approaches in predictive process monitoring, in: Advanced Infor-
mation Systems Engineering - 35th International Conference, CAiSE 2023, Proceedings,
volume 13901 of LNCS, Springer, 2023, pp. 558–574.
[9] A. Buliga, C. D. Francescomarino, C. Ghidini, I. Donadello, F. M. Maggi, Guiding the
generation of counterfactual explanations through temporal background knowledge for
predictive process monitoring, CoRR abs/2403.11642 (2024). arXiv:2403.11642 .
[10] C. Di Ciccio, M. Montali, Declarative process specifications: Reasoning, discovery, moni-
toring, in: Process Mining Handbook, volume 448 of LNBIP, Springer, 2022, pp. 108–152.
[11] M. Vazifehdoostirani, L. Genga, X. Lu, R. Verhoeven, H. van Laarhoven, R. M. Dijkman,
Interactive multi-interest process pattern discovery, in: Business Process Management -
21st Int. Conf., BPM 2023, Proceedings, volume 14159 of LNCS, Springer, 2023, pp. 303–319.
[12] W. Rizzi, C. D. Francescomarino, F. M. Maggi, Explainability in predictive process mon-
itoring: When understanding helps improving, in: Business Process Management Fo-
rum - BPM Forum 2020, Seville, Spain, September 13-18, 2020, Proceedings, volume 392
of Lecture Notes in Business Information Processing, Springer, 2020, pp. 141–158. URL:
https://doi.org/10.1007/978-3-030-58638-6_9. doi:10.1007/978- 3- 030- 58638- 6\_9 .
[13] M. Ronzani, R. Ferrod, C. Di Francescomarino, E. Sulis, R. Aringhieri, G. Boella, E. Brunetti,
L. Di Caro, M. Dragoni, C. Ghidini, R. Marinello, Unstructured data in predictive process
monitoring: Lexicographic and semantic mapping to ICD-9-CM codes for the home
hospitalization service, in: AIxIA 2021, Revised Selected Papers, volume 13196 of Lecture
Notes in Computer Science, Springer, 2021, pp. 700–715.