43 citations · 181 across the 31 of their papers we have counts for
33 papers
Emergent Behavior and Uncertainty in IoT-Enhanced Business Processes: Challenges and Future Directions
Marco Pegoraro, Sara Pettinari, Ivan Compagnucci +2
IoT-enhanced business processes are characterized by high complexity due to heterogeneous actors, varying levels of autonomy among participating systems, continuously evolving exec…
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Marco Pegoraro, Jonas Seng, Dustin Heller +2
Disciplines such as business process management and process mining aid organizations by discovering insights about processes on the basis of recorded event data. However, an obstac…
Inference-time optimization for experiment-grounded protein ensemble generation
Advaith Maddipatla, Anar Rzayev, Marco Pegoraro +5
Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data. Recent expe…
FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
Marco Pegoraro, Evan Atherton, Bruno Roy +2
Learning natural body motion remains challenging due to the strong coupling between spatial geometry and temporal dynamics. Embedding motion in phase manifolds, latent spaces that…
ProReco: A Process Discovery Recommender System
Tsung-Hao Huang, Tarek Junied, Marco Pegoraro +1
Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last…
Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction
Francesco Vitale, Marco Pegoraro, Wil M. P. van der Aalst +1
The business processes of organizations may deviate from normal control flow due to disruptive anomalies, including unknown, skipped, and wrongly-ordered activities. To identify th…
Ranking the Top-K Realizations of Stochastically Known Event Logs
Arvid Lepsien, Marco Pegoraro, Frederik Fonger +3
Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochasticall…
Latent Functional Maps: a spectral framework for representation alignment
Marco Fumero, Marco Pegoraro, Valentino Maiorca +2
Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these representational spaces is an ongoing challenge. By integrat…
Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study
Harry H. Beyel, Marlo Verket, Viki Peeva +5
Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain. There is high diversity considering the variety…
OCEL (Object-Centric Event Log) 2.0 Specification
Alessandro Berti, Istvan Koren, Jan Niklas Adams +10
Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM tech…
Geometric Epitope and Paratope Prediction
Marco Pegoraro, Clémentine Dominé, Emanuele Rodolà +2
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting…
Applying Process Mining on Scientific Workflows: a Case Study on High Performance Computing Data
Zahra Sadeghibogar, Alessandro Berti, Marco Pegoraro +1
Computer-based scientific experiments are becoming increasingly data-intensive, necessitating the use of High-Performance Computing (HPC) clusters to handle large scientific workfl…
Vector Quantile Regression on Manifolds
Marco Pegoraro, Sanketh Vedula, Aviv A. Rosenberg +3
Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features. QR is limited by the assum…
Performance-Preserving Event Log Sampling for Predictive Monitoring
Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park +3
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant intere…
Resolving Uncertain Case Identifiers in Interaction Logs: A User Study
Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann +1
Modern software systems are able to record vast amounts of user actions, stored for later analysis. One of the main types of such user interaction data is click data: the digital t…
Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study
Elisabetta Benevento, Marco Pegoraro, Mattia Antoniazzi +6
The discipline of process mining has a solid track record of successful applications to the healthcare domain. Within such research space, we conducted a case study related to the…
Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques
Marco Pegoraro
Process mining is a subfield of process science that analyzes event data collected in databases called event logs. Recently, novel types of event data have become of interest due t…
Spectral Maps for Learning on Subgraphs
Marco Pegoraro, Riccardo Marin, Arianna Rampini +3
In graph learning, maps between graphs and their subgraphs frequently arise. For instance, when coarsening or rewiring operations are present along the pipeline, one needs to keep…
Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data
Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann +1
Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting…
Process Mining on Uncertain Event Data
Marco Pegoraro
With the widespread adoption of process mining in organizations, the field of process science is seeing an increase in the demand for ad-hoc analysis techniques of non-standard eve…
An XES Extension for Uncertain Event Data
Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are of…
Event Log Sampling for Predictive Monitoring
Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park +3
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant intere…
A Web-Based Tool for Comparative Process Mining
Madhavi Bangalore Shankara Narayana, Elisabetta Benevento, Marco Pegoraro +5
Process mining techniques enable the analysis of a wide variety of processes using event data. Among the available process mining techniques, most consider a single process perspec…
Analyzing Medical Data with Process Mining: a COVID-19 Case Study
Marco Pegoraro, Madhavi Bangalore Shankara Narayana, Elisabetta Benevento +3
The recent increase in the availability of medical data, possible through automation and digitization of medical equipment, has enabled more accurate and complete analysis on patie…