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.
no code implementations • 7 Mar 2024 • Humam Kourani, Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst
ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques.
no code implementations • 6 Nov 2023 • Viki Peeva, Wil M. P. van der Aalst
In recent years, process mining emerged as a proven technology to analyze and improve operational processes.
no code implementations • 16 Oct 2023 • Gyunam Park, Sevde Aydin, Cuneyt Ugur, Wil M. P. van der Aalst
Process mining, a technique turning event data into business process insights, has traditionally operated on the assumption that each event corresponds to a singular case or object.
1 code implementation • 4 Oct 2023 • Majid Rafiei, Duygu Bayrak, Mahsa Pourbafrani, Gyunam Park, Hayyan Helal, Gerhard Lakemeyer, Wil M. P. van der Aalst
In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students.
no code implementations • 29 Mar 2023 • Majid Rafiei, Frederik Wangelik, Mahsa Pourbafrani, Wil M. P. van der Aalst
Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant.
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 • 4 Jan 2023 • Tsung-Hao Huang, Wil M. P. van der Aalst
In this paper, we investigate the effect of different ordering strategies on the discovered models (w. r. t.
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.
1 code implementation • 30 Oct 2022 • Gyunam Park, Aaron Küsters, Mara Tews, Cameron Pitsch, Jonathan Schneider, Wil M. P. van der Aalst
By predicting the decision, one can take proactive actions to improve the process.
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.
1 code implementation • 2 Sep 2022 • Jan Niklas Adams, Gyunam Park, Sergej Levich, Daniel Schuster, Wil M. P. van der Aalst
The flattening process is lossy, leading to inaccurate features extracted from flattened data.
no code implementations • 29 Aug 2022 • Christian Kohlschmidt, Mahnaz Sadat Qafari, Wil M. P. van der Aalst
We define a process enhancement area as a set of situations (process instances or prefixes of process instances) where the process performance is surprising.
no code implementations • 5 Aug 2022 • Jan Niklas Adams, Daniel Schuster, Seth Schmitz, Günther Schuh, Wil M. P. van der Aalst
Equivalent process executions with respect to the event's activity are object-centric variants, i. e., a generalization of variants in traditional process mining.
no code implementations • 26 Jul 2022 • Anahita Farhang Ghahfarokhi, Fatemeh Akoochekian, Fareed Zandkarimi, Wil M. P. van der Aalst
Therefore, several clustering techniques have been proposed on top of traditional event logs (i. e., event logs with a single case notion) to reduce the complexity of process models and discover homogeneous subsets of cases.
no code implementations • 20 Jul 2022 • Timo Rohrer, Anahita Farhang Ghahfarokhi, Mohamed Behery, Gerhard Lakemeyer, Wil M. P. van der Aalst
Objects in OCEL can have attributes that are useful in predicting the next event and timestamp, such as a priority class attribute for an object type package indicating slower or faster processing.
1 code implementation • 11 Jun 2022 • Gyunam Park, Janik-Vasily Benzin, Wil M. P. van der Aalst
We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods.
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 • 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 • 24 Mar 2022 • Gyunam Park, Marco Comuzzi, Wil M. P. van der Aalst
In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates.
no code implementations • 6 Oct 2021 • Jan Niklas Adams, Wil M. P. van der Aalst
Our precision and fitness notions are an appropriate way to generalize quality measures to the object-centric setting since we are able to consider multiple case notions, their dependencies and their interactions.
no code implementations • 6 Oct 2021 • Andrew Pery, Majid Rafiei, Michael Simon, Wil M. P. van der Aalst
The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks.
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.
no code implementations • 31 Jul 2021 • Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
Previously, an incremental discovery approach has been introduced where a model, considered to be under construction, gets incrementally extended by user-selected process behavior.
no code implementations • 7 Jun 2021 • Wil M. P. van der Aalst
The approach presented in this paper provides a novel perspective enabling new analysis techniques for free-choice nets that do not need to be well-formed.
1 code implementation • 27 May 2021 • Jan Niklas Adams, Sebastiaan J. van Zelst, Lara Quack, Kathrin Hausmann, Wil M. P. van der Aalst, Thomas Rose
We propose a framework that adds an explainability level onto concept drift detection in process mining and provides insights into the cause-effect relationships behind significant changes.
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.
no code implementations • 12 Mar 2021 • Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M. P. van der Aalst
Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes.
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 • 24 Nov 2020 • Jing Yang, Chun Ouyang, Wil M. P. van der Aalst, Arthur H. M. ter Hofstede, Yang Yu
We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.
1 code implementation • 5 Oct 2020 • Wil M. P. van der Aalst, Alessandro Berti
Techniques to discover Petri nets from event data assume precisely one case identifier per event.
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 • 2 Dec 2019 • Mohammadreza Fani Sani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
This paper proposes new approximation techniques to compute approximated conformance checking values close to exact solution values in a faster time.
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.
no code implementations • 30 Aug 2019 • Anja F. Syring, Niek Tax, Wil M. P. van der Aalst
Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process.
no code implementations • 3 Nov 2017 • Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst
We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques.
no code implementations • 17 Oct 2017 • Maikel Leemans, Wil M. P. van der Aalst, Mark G. J. van den Brand
This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems.
no code implementations • 7 Jun 2017 • Maikel L. van Eck, Natalia Sidorova, Wil M. P. van der Aalst
For example, we are able to highlight strongly correlated behaviours in different artifacts.
no code implementations • 25 May 2017 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts.
no code implementations • 25 May 2017 • Niek Tax, Emin Alasgarov, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma
Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models.
no code implementations • 3 May 2017 • Niek Tax, Xixi Lu, Natalia Sidorova, Dirk Fahland, Wil M. P. van der Aalst
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log.
no code implementations • 25 Apr 2017 • Sebastiaan J. van Zelst, Boudewijn F. van Dongen, Wil M. P. van der Aalst
The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data.
no code implementations • 21 Mar 2017 • Niek Tax, Benjamin Dalmas, Natalia Sidorova, Wil M. P. van der Aalst, Sylvie Norre
Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes.
no code implementations • 10 Oct 2016 • Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i. e. subsets of possible events are taken into account to create so-called local process models.
no code implementations • 23 Jun 2016 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity.
no code implementations • 23 Jun 2016 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective.
no code implementations • 20 Jun 2016 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining.