Search Results for author: Wil M. P. van der Aalst

Found 30 papers, 3 papers with code

Analyzing Process-Aware Information System Updates Using Digital Twins of Organizations

no code implementations24 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.

Precision and Fitness in Object-Centric Process Mining

no code implementations6 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.

Model Discovery

Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

no code implementations6 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.

Probability Estimation of Uncertain Process Trace Realizations

no code implementations19 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.

Freezing Sub-Models During Incremental Process Discovery: Extended Version

no code implementations31 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.

Free-Choice Nets With Home Clusters Are Lucent

no code implementations7 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.

A Framework for Explainable Concept Drift Detection in Process Mining

1 code implementation27 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.

Text-Aware Predictive Monitoring of Business Processes

no code implementations20 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.

Process Comparison Using Object-Centric Process Cubes

no code implementations12 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.

PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data

1 code implementation9 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.

OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs

no code implementations24 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.

Model Discovery

Discovering Object-Centric Petri Nets

1 code implementation5 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.

Conformance Checking over Uncertain Event Data

no code implementations29 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.

Conformance Checking Approximation using Subset Selection and Edit Distance

no code implementations2 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.

Mining Uncertain Event Data in Process Mining

no code implementations20 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.

Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)

no code implementations30 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.

Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities

no code implementations3 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.

Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)

no code implementations17 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.

Model Discovery

Guided Interaction Exploration in Artifact-centric Process Models

no code implementations7 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.

Generating Time-Based Label Refinements to Discover More Precise Process Models

no code implementations25 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.

Mining Process Model Descriptions of Daily Life through Event Abstraction

no code implementations25 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.

The Imprecisions of Precision Measures in Process Mining

no code implementations3 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.

Event Stream-Based Process Discovery using Abstract Representations

no code implementations25 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.

Interest-Driven Discovery of Local Process Models

no code implementations21 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.

Heuristic Approaches for Generating Local Process Models through Log Projections

no code implementations10 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.

Log-based Evaluation of Label Splits for Process Models

no code implementations23 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.

Event Abstraction for Process Mining using Supervised Learning Techniques

no code implementations23 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.

Mining Local Process Models

no code implementations20 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.

Model Discovery Sequential Pattern Mining

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