Wil Van der Aalst

Wil Van der Aalst
RWTH Aachen University · Lehrstuhl für Informatik 9 / Process and Data Science

Professor
Always looking for bright students, PhDs, Postdocs, and programmers that would like to work on process & data science!

About

1,431
Publications
653,259
Reads
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84,615
Citations
Introduction
Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis. His research interests include process mining, business process management, workflow automation, process modeling, and simulation. Van der Aalst is an IFIP Fellow, IEEE Fellow, ACM Fellow, serves on the editorial boards of a dozen scientific journals.

Publications

Publications (1,431)
Preprint
Full-text available
A deviation detection aims to detect deviating process instances, e.g., patients in the healthcare process and products in the manufacturing process. A business process of an organization is executed in various contextual situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of semiconductor chip shortage in the case of automobi...
Article
Full-text available
The Internet of Things promises to bring significant improvements to manufacturing by facilitating the integration of manufacturing devices to collect sensor data and to control production processes. In contrast to previous industrial revolutions , today's change is driven by applied computer science technologies on several layers: Improved interfa...
Conference Paper
Full-text available
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 in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data....
Article
Process mining aims to improve operational processes in a data-driven manner. To this end, process mining offers methods and techniques for systematically analyzing event data. These data are generated during the execution of processes and stored in organizations' information systems. Process discovery, a key discipline in process mining, comprises...
Chapter
Open-source process mining provides many algorithms for the analysis of event data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM). However, compared to commercial tools, they lack the performance and struggle to analyze large amounts of data. This paper presents PM4Py-GPU, a Python process mining library based on the NVID...
Chapter
Process mining techniques make the underlying processes in organizations transparent. Historical event data are used to perform conformance checking and performance analyses. Analyzing a single process and providing visual insights has been the focus of most process mining techniques. However, comparing two processes or a single process in differen...
Chapter
Full-text available
Process mining techniques provide insights into operational processes by systematically analyzing event data generated during process execution. These insights are used to improve processes, for instance, in terms of runtime, conformity, or resource allocation. Time-based performance analysis of processes is a key use case of process mining. This p...
Preprint
Full-text available
Performance analysis in process mining aims to provide insights on the performance of a business process by using a process model as a formal representation of the process. Such insights are reliably interpreted by process analysts in the context of a model with formal semantics. Existing techniques for performance analysis assume that a single cas...
Preprint
Full-text available
Standard process discovery algorithms find a single process model that describes all traces in the event log from start to end as best as possible. However, when the event log contains highly diverse behavior, they fail to find a suitable model, i.e., a so-called "flower" or "spaghetti" model is returned. In these cases, discovering local process m...
Preprint
Full-text available
Open-source process mining provides many algorithms for the analysis of event data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM). However, compared to commercial tools, they lack the performance and struggle to analyze large amounts of data. This paper presents PM4Py-GPU, a Python process mining library based on the NVID...
Preprint
Full-text available
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 in a type of user interaction data of en called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data....
Preprint
Full-text available
In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language. In this paper, we present an extension of the eST-Miner process discovery algorithm. This approach computes a...
Conference Paper
Full-text available
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 patients' medical data through many branches of data science. In particular, medical records that include timestamps showing the history of a patient have enabled the represen...
Article
Full-text available
Process Mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of Process Mining in organisations has been far more limited. In particular, there is limited understanding of the...
Preprint
Full-text available
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 perspective at a time-in the shape of a model or log. In this paper, we have developed a tool that can compare and visualize the same process under different constraints, allowi...
Article
The focus of this paper is on how data quality can affect business process discovery in real complex environments, which is a major factor determining the success in any data-driven Business Process Management project. Many real-life event logs, especially healthcare ones, can suffer from several data quality issues, some of which cannot be solved...
Chapter
Full-text available
Process mining enables the discovery of actionable insights from event data of organizations. Process analysis techniques typically focus on process executions at detailed, i.e., fine-grained levels, which might lead to missed insights. For instance, the relation between the waiting time of process instances and the current states of the process in...
Conference Paper
Full-text available
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 interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often ineffi...
Conference Paper
Full-text available
Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs. Recently, uncertain event logs have become of interest, which contain non-deterministic and stochastic event attributes that may represent many possible real-life scenarios. In this paper, we present a method to reliably estimate the...
Preprint
Full-text available
Digital transformation often entails small-scale changes to information systems supporting the execution of business processes. These changes may increase the operational frictions in process execution, which decreases the process performance. The contributions in the literature providing support to the tracking and impact analysis of small-scale c...
Article
Full-text available
In order to streamline business processes and increase competitiveness, organizations need to have a deep insight into the resources that they deploy. Among others, they need to understand how these resources act in groups to achieve organizational outcomes. Accurate and timely information is a sine qua non to achieve this understanding. Process mi...
Chapter
With the advent of Industry 4.0, increasing amounts of data on operational processes (e.g., manufacturing processes) become available. These processes can involve hundreds of different materials for a relatively small number of manufactured special-purpose machines rendering classical process discovery and analysis techniques infeasible. However, i...
Preprint
Full-text available
Object-centric process mining provides a set of techniques for the analysis of event data where events are associated to several objects. To store Object-centric Event Logs (OCELs), the JSON-OCEL and JSON-XML formats have been recently proposed. However, the proposed implementations of the OCEL are file-based. This means that the entire file needs...
Preprint
Full-text available
Object-centric process mining provides a more holistic view of processes where we analyze processes with multiple case notions. However, most object-centric process mining techniques consider the whole event log rather than the comparison of existing behaviors in the log. In this paper, we introduce a stand-alone object-centric process cube tool bu...
Article
Full-text available
Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by s...
Article
Given a process model and an event log, conformance checking aims to relate the two together, e.g. to detect discrepancies between them. For the synchronous product net of the process and a log trace, we can assign different costs to a synchronous move, and a move in the log or model. By computing a path through this (synchronous) product net, whil...
Article
Full-text available
Robotic Process Automation (RPA) has lowered the threshold for process automation. Repetitive tasks done by people are handed over to software robots. For RPA, there is no need to change or replace the pre-existing information systems. Instead, software robots replace users by interacting directly with the user interfaces normally operated by human...
Conference Paper
Full-text available
We use sequences of t-induced T-nets and p-induced P-nets to convert free-choice nets into T-nets and P-nets while preserving properties such as well-formedness, liveness, lucency, pc-safety, and perpetuality. The approach is general and can be applied to different properties. This allows for more systematic proofs that "peel off" non-trivial parts...
Preprint
Full-text available
Event data provide the main source of information for analyzing and improving processes in organizations. Process mining techniques capture the state of running processes w.r.t. various aspects, such as activity-flow and performance metrics. The next step for process owners is to take the provided insights and turn them into actions in order to imp...
Article
To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to...
Chapter
Process mining starts from event data. The ordering of events is vital for the discovery of process models. However, the timestamps of events may be unreliable or imprecise. To further complicate matters, also causally unrelated events may be ordered in time. The fact that one event is followed by another does not imply that the former causes the l...
Chapter
The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process...
Chapter
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 a...
Chapter
Full-text available
Although the popularity and adoption of process mining techniques grew rapidly in recent years, a large portion of effort invested in process mining initiatives is still consumed by event data extraction and transformation rather than process analysis. The IEEE Task Force on Process Mining conducted a study focused on the challenges faced during ev...
Chapter
Full-text available
Executing operational processes generates event data, which contain information on the executed process activities. Process mining techniques allow to systematically analyze event data to gain insights that are then used to optimize processes. Visual analytics for event data are essential for the application of process mining. Visualizing unique pr...
Chapter
Process mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states ( Predictive Process Monitoring ). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations...
Chapter
The size of execution data available for process mining analysis grows several orders of magnitude every couple of years. Extracting and selecting the relevant data to enable process mining remains a challenging and time-consuming task. In fact, it is the biggest handicap when applying process mining and other forms of process-centric analysis. Thi...
Chapter
Full-text available
Process discovery aims to learn a process model from observed process behavior. From a user’s perspective, most discovery algorithms work like a black box. Besides parameter tuning, there is no interaction between the user and the algorithm. Interactive process discovery allows the user to exploit domain knowledge and to guide the discovery process...
Conference Paper
Full-text available
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 often tainted by noise, errors, and missing data. Recently, a novel body of research has emerged, with the aim to address and analyze a class of anomalies known as uncertai...
Preprint
Full-text available
The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process...
Preprint
Full-text available
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 a...
Preprint
Full-text available
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 a...
Preprint
Full-text available
Traditional process mining considers only one single case notion and discovers and analyzes models based on this. However, a single case notion is often not a realistic assumption in practice. Multiple case notions might interact and influence each other in a process. Object-centric process mining introduces the techniques and concepts to handle mu...
Preprint
Full-text available
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 interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often ineffi...
Preprint
Full-text available
Executing operational processes generates event data, which contain information on the executed process activities. Process mining techniques allow to systematically analyze event data to gain insights that are then used to optimize processes. Visual analytics for event data are essential for the application of process mining. Visualizing unique pr...
Preprint
Full-text available
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 interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often ineffi...
Conference Paper
Full-text available
Process discovery from event logs as well as process prediction using process models at runtime are increasingly important aspects to improve the operation of digital twins of complex systems. The integration of process mining functionalities with model-driven digital twin architectures raises the question which models are important for the model-d...
Article
Full-text available
Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the...
Preprint
Full-text available
Process mining techniques use event data to describe business processes, where the provided insights are used for predicting processes' future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations i...
Preprint
Full-text available
A Digital Twin of an Organization (DTO) is a mirrored representation of an organization, aiming to improve the business process of the organization by providing a transparent view over the process and automating management actions to deal with existing and potential risks. Unlike wide applications of digital twins to product design and predictive m...
Article
Full-text available
The only constant in our world is change. Why is there not a field of science that explicitly studies continuous change? We propose the establishment of process science, a field that studies processes: coherent series of changes, both man-made and naturally occurring, that unfold over time and occur at various levels. Process science is concerned w...
Chapter
Through its smart contract capabilities, blockchain has become a technology for automating cross-organizational processes on a neutral platform. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. While researchers have recently created techniques for the challenging task of extracting...
Chapter
Full-text available
Rapidly changing business environments expose companies to high levels of uncertainty. This uncertainty manifests itself in significant changes that tend to occur over the lifetime of a process and possibly affect its performance. It is important to understand the root causes of such changes since this allows us to react to change or anticipate fut...
Chapter
Workforce analytics brings data-driven methods to organizations for deriving insights from employee-related data and supports decision making. However, it faces an open challenge of lacking the capability to analyze the behavior of employee groups in order to understand organizational performance. This paper proposes a novel notion of work profiles...
Chapter
Full-text available
Process mining dramatically changed the way we look at process models and operational processes. Even seemingly simple processes like Purchase-to-Pay (P2P) and Order-to-Cash (O2C) are often amazingly complex, and traditional hand-made process models fail to capture the true fabric of such processes. Many processes are inherently concurrent and invo...
Preprint
Full-text available
Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs. Recently, uncertain event logs have become of interest, which contain non-deterministic and stochastic event attributes that may represent many possible real-life scenarios. In this paper, we present a method to reliably estimate the...