no code implementations • 11 Mar 2024 • Harry H. Beyel, Marlo Verket, Viki Peeva, Christian Rennert, Marco Pegoraro, Katharina Schütt, Wil M. P. van der Aalst, Nikolaus Marx
Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain.
1 code implementation • 3 Jul 2023 • Marco Pegoraro, Sanketh Vedula, Aviv A. Rosenberg, Irene Tallini, Emanuele Rodolà, Alex M. Bronstein
Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features.
no code implementations • 28 May 2023 • Marco Pegoraro, Clémentine Dominé, Emanuele Rodolà, Petar Veličković, Andreea Deac
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules.
no code implementations • 18 Jan 2023 • Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances.
no code implementations • 21 Nov 2022 • Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann, Wil M. P. van der Aalst
Modern software systems are able to record vast amounts of user actions, stored for later analysis.
no code implementations • 22 Sep 2022 • Elisabetta Benevento, Marco Pegoraro, Mattia Antoniazzi, Harry H. Beyel, Viki Peeva, Paul Balfanz, Wil M. P. van der Aalst, Lukas Martin, Gernot Marx
The aim of this work is twofold: developing a normative model representing the clinical guidelines for the treatment of COVID-19 patients, and analyzing the adherence of the observed behavior (recorded in the information system of the hospital) to such guidelines.
no code implementations • 30 May 2022 • Marco Pegoraro, Riccardo Marin, Arianna Rampini, Simone Melzi, Luca Cosmo, Emanuele Rodolà
We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism.
no code implementations • 10 May 2022 • Marco Pegoraro
Process mining is a subfield of process science that analyzes event data collected in databases called event logs.
no code implementations • 8 Apr 2022 • Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann, Wil M. P. van der Aalst
Among the many sources of event data available today, a prominent one is user interaction data.
no code implementations • 8 Apr 2022 • 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 event data.
no code implementations • 4 Apr 2022 • Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances.
no code implementations • 19 Aug 2021 • Marco Pegoraro, Bianka Bakullari, Merih Seran Uysal, Wil M. P. van der Aalst
Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs.
1 code implementation • 4 Aug 2021 • Marco Pegoraro, Simone Melzi, Umberto Castellani, Riccardo Marin, Emanuele Rodolà
In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand.
no code implementations • 20 Apr 2021 • Marco Pegoraro, Merih Seran Uysal, David Benedikt Georgi, Wil M. P. van der Aalst
The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems.
1 code implementation • 9 Mar 2021 • Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
The discipline of process mining aims to study processes in a data-driven manner by analyzing historical process executions, often employing Petri nets.
no code implementations • 29 Sep 2020 • Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems.
no code implementations • 20 Sep 2019 • Marco Pegoraro, Wil M. P. van der Aalst
Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs.