Wil Van der Aalst

RWTH Aachen University · Lehrstuhl für Informatik 9 / Process and Data Science

To improve processes in logistics, it is crucial to understand the factors influencing performance. To achieve this, process mining utilizes event data to extract insights into operational processes. In this paper, we present a case study conducted in an air cargo terminal, where process mining is applied to event data collected during package dist...

Processes tend to interact with other processes and operate on various objects of different types. These objects can influence each other creating dependencies between sub-processes. Analyzing the conformance of such complex processes challenges traditional conformance-checking approaches because they assume a single-case identifier for a process....

The Internet of Things (IoT) has empowered enterprises to optimize process efficiency and productivity by analyzing sensor data. This can be achieved with process mining, a technology that enables organizations to extract valuable insights from data recorded during process execution, referred to as event data in a process mining context. In our cas...

Process mining is a family of techniques that provide tools for gaining insights from processes in, for example, business, industrial, healthcare and administrative settings. Process discovery, as a field of process mining, aims to give a process model that describes a process given by an event log. A process model describes an underlying process w...

RWTH Industry AIxChange Workshop | Hybrid Intelligence | How to distribute work over people and algorithms? | Panel discussion | Denis Golovin, automaited | Prof. Gerhard Lakemeyer, RWTH Aachen University | Dr. Markus Ohlenforst, IconPro | Dr. Malte Persike, RWTH Aachen University | Dr. Friedrich Wolf-Monheim, Ford Motor Company | Moderation: Prof....

The Internet of Production (IoP) promises to be the answer to major challenges facing the Industrial Internet of Things (IIoT) and Industry 4.0. The lack of inter-company communication channels and standards, the need for heightened safety in Human Robot Collaboration (HRC) scenarios, and the opacity of data-driven decision support systems are only...

Business processes may face a variety of problems due to the number of tasks that need to be handled within short time periods, resources' workload and working patterns, as well as bottlenecks. These problems may arise locally and be short-lived, but as the process is forced to operate outside its standard capacity, the effect on the underlying pro...

Process mining endeavors to extract fact-based insights into processes based on event data stored in information systems. Due to the variety of processes in different fields and organizations, there does not exist a universal technique to allow for putting the process mining outcome directly into action. Various techniques have been developed to su...

Process discovery learns process models from event data and is a crucial discipline within process mining. Most existing approaches are fully automated, i.e., event data is provided, and a process model is returned. Thus, process analysts cannot interact and intervene besides parameter settings. In contrast, Incremental Process Discovery (IPD) enab...

Simulation is a powerful tool to explore and analyze business processes and their potential improvements. Recorded event data allow for the generation of data-driven simulation models using process mining. The accuracy of existing approaches, however, remains a challenge. Various efforts are being made to improve the quality of the used data and te...

Business processes may face a variety of problems due to the number of tasks that need to be handled within short time periods, resources’ workload and working patterns, as well as bottlenecks. These problems may arise locally and be short-lived, but as the process is forced to operate outside its standard capacity, the effect on the underlying pro...

Logistics processes ensure that the right product is at the right location at the right time in the right quantity. Their efficiency is crucial to industrial operations, as they generate costs while not adding value to the product. Process mining techniques improve processes using real-life data. However, the application of process mining to logist...

Business Process Management (BPM) heavily relies on event logs for process mining. However, traditional event logs may not always be available or may be harder to obtain for unlogged or unconventionally logged activities. To overcome these limitations, network traffic data can be used as an alternative source for constructing event logs. However, i...

SLURMminer is a tool designed to analyze SLURM systems in High-Performance Computing (HPC) clusters. It utilizes process mining techniques to generate event logs, extract process models, and visualize critical business intelligence metrics. The tool's unique log extraction approach for SLURM clusters allows for a detailed analysis of jobs and workf...

As scientific experiments grow more data-intensive, HPC clusters have become the go-to infrastructure for handling expansive scientific workflows. This work explores the potential of process mining on SLURM-managed HPC cluster logs, targeting the description of workflows and bottleneck identification. The correlation of system-recorded jobs, consid...

Event logs, as considered in process mining, document a large number of individual process executions. Moreover, each process execution consists of various executed activities. To cope with the vast amount of process executions in event logs, the concept of variants exists that group process executions with identical ordering relations among their...

Process mining techniques have proven crucial in identifying performance and compliance issues. Traditional process mining, however, is primarily case-centric and does not fully capture the complexity of real-life information systems, leading to a growing interest in object-centric process mining. This paper presents a novel graph-based approach fo...

The purchase-to-pay (P2P) process is one of the core business processes in any organization. It ensures the correct and efficient provisioning of materials and services. An efficient P2P process reduces operational costs by ensuring discounts, avoiding late payments, and choosing the optimal supplier for the goods. Process mining techniques help pr...

Changes in society require changes in our industrial production. In order to remain competitive in the future, the masses of data available in production must be used urgently. This is still a challenge because data are often not accessible or understandable. Therefore, we developed the Internet of Production (IoP) concept which aims to collect, un...

Computer-based scientific experiments are becoming increasingly data-intensive. High-Performance Computing (HPC) clusters are ideal for executing large scientific experiment workflows. Executing large scientific workflows in an HPC cluster leads to complex flows of data and control within the system, which are difficult to analyze. This paper prese...

Large Language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparable to humans for many proficient tasks. The analysis of business processes could benefit from a natural process querying language and using the domain knowledge on which...

Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs. Organizations can gain various insights into their business processes by using process mining techniques. Such techniques use event logs extracted from relational databases supporting the business process as input. However, extractin...

Different approaches are proposed for simulating processes in process mining. There are open challenges while designing the simulation models of processes: (1) the quality of the designed models is mostly evaluated using simulation results, and the models themselves do not get validated, (2) the choice of process aspects to be considered in the sim...

Discrete-event simulation has been around for over half a century with applications in production, healthcare, logistics, transportation, etc. However, it is still challenging to create a reliable simulation model that mimics the actual process well and allows for “what-if” questions. Process mining allows for the automated discovery of stochastic...

Process mining provides techniques to learn models from event data. These models can be descriptive (e.g., Petri nets) or predictive (e.g., neural networks). The learned models offer operational support to process owners by conformance checking, process enhancement, or predictive monitoring. However, processes are frequently subject to significant...

The analysis of fairness in process mining is a significant aspect of data-driven decision-making, yet the advancement in this field is constrained due to the scarcity of event data that incorporates fairness considerations. To bridge this gap, we present a collection of simulated event logs, spanning four critical domains, which encapsulate a vari...

Process mining enables companies to extract insights into the process execution from event data. Event data that are stored in information systems are often too fine-grained. When process mining techniques are applied to such system-level event data, the outcomes are often overly complex for human analysts to interpret. To address this issue, numer...

In process discovery, the goal is to find, for a given event log, the model describing the underlyingprocess. While process models can be represented in a variety of ways, Petri nets form a theoreticallywell-explored description language. A Petri net describes the underlying process well if it allows forall relevant behavior (fitness) and, at the s...

Different approaches are proposed for simulating processes in process mining. There are open challenges while designing the simulation models of processes: (1) the quality of the designed models is mostly evaluated using simulation results, and the models themselves do not get validated, (2) the choice of process aspects to be considered in the sim...

Process discovery aims to discover models to explain the behaviors of information systems. The Inductive Miner (IM) discovery algorithm is able to discover process models with desirable properties: free-choiceness and soundness. Moreover, a family of variations makes IM practical for real-life applications. Due to the advantages, IM is regarded as...

The Alpha algorithm was the first process discovery algorithm that was able to discover process models with concurrency based on incomplete event data while still providing formal guarantees. However, as was stated in the original paper, practical applicability is limited when dealing with exceptional behavior and processes that cannot be described...

Process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant. State-of-the-art research mainly focuses on providing privacy guarantees, e.g., differential privacy, for trace variants that are...

The Internet of Production (IoP) promises to be the answer to major challenges facing the Industrial Internet of Things (IIoT) and Industry 4.0. The lack of inter-company communication channels and standards, the need for heightened safety in Human Robot Collaboration (HRC) scenarios, and the opacity of data-driven decision support systems are only...

Processes tend to interact with other processes and operate on various objects of different types. These objects can influence each other creating dependencies between sub-processes. Analyzing the conformance of such complex processes challenges traditional conformance-checking approaches because they assume a single-case identifier for a process....

Process discovery is an essential discipline within process mining, which deals with the data-driven generation of insights into operational processes. From event data that capture historical process executions, process discovery algorithms learn a process model describing the execution of the various activities involved. Such discovered models are...

The transition towards more sustainable practices requires companies to assess their impact on the social and ecological environment and establish new processes in complex inter-organisational systems. Process mining is a collection of data-driven techniques to visualise, analyse and improve business processes. Its potential to increase sustainable...

Digitization in the field of production is fragmented in very different domains, ranging from materials to production technology to process and business models. Each domain comes with specialized knowledge, often incorporated into mathematical models. This heterogeneity makes it hard to naively exploit advances in data-driven machine learning that...

Process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant. State-of-the-art research mainly focuses on providing privacy guarantees, e.g., differential privacy, for trace variants that are...

Process mining is a set of techniques that are used by organizations to understand and improve their operational processes. The first essential step in designing any process reengineering procedure is to find process improvement opportunities. In existing work, it is usually assumed that the set of problematic process instances in which an undesira...

In the area of industrial process mining, privacy-preserving event data publication is becoming increasingly relevant. Consequently, the trade-off between high data utility and quantifiable privacy poses new challenges. State-of-the-art research mainly focuses on differentially private trace variant construction based on prefix expansion methods. H...

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 Intensive Care Unit (ICU) ward of the Uniklinik Aachen hospital in Germany. The aim of this work is twofold: developing a normative model representing the clinical guidel...

Constraint monitoring aims to monitor the violation of constraints in business processes, e.g., an invoice should be cleared within 48 h after the corresponding goods receipt, by analyzing event data. Existing techniques for constraint monitoring assume that a single case notion exists in a business process, e.g., a patient in a healthcare process,...

This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from exp...

Organizations increasingly use process mining techniques to gain insight into their processes. Process mining techniques can be used to monitor and/or enhance processes. However, the impact of processes on the people involved, in terms of unfair discrimination, has not been studied. Another neglected area is the impact of applying process mining te...

When multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including at...

Event logs capture information about executed activities. However, they do not capture information about activities that could have been performed, i.e., activities that were enabled during a process. Event logs containing information on enabled activities are called translucent event logs. Although it is possible to extract translucent event logs...

Process discovery aims to learn process models from observed behaviors, i.e., event logs, in the information systems. The discovered models serve as the starting point for process mining techniques that are used to address performance and compliance problems. Compared to the state-of-the-art Inductive Miner, the algorithm applying synthesis rules f...

Several decision points exist in business processes (e.g., whether a purchase order needs a manager’s approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than €500 needs a manager approval). Decision mining in process mining aims to describe/predict the rou...

Recorded event data of processes inside organizations is a valuable source for providing insights and information using process mining. Most techniques analyze process executions at detailed levels, e.g., process instances, which may result in missing insights. Techniques at detailed levels using detailed event data should be complemented by techni...

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, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is...

Changes in society require changes in our industrial production. In order to remain competitive in the future, the masses of data available in production must be used urgently. This is still a challenge because data are often not accessible or understandable. Therefore, we developed the Internet of Production (IoP) concept which aims to collect, un...

Process discovery is one of the most challenging tasks in process mining. Based on event data, a process discovery approach generates a process model that captures the behavior recorded in the data. The hybrid miner is a two-step process discovery approach that creates a balance between the advantages of formal modeling and the necessity of remaini...

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, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is...

Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs.Organizations can gain various insights into their business processes by using process mining techniques.Such techniques use event logs extracted from relational databases supporting the business process as input.However, extracting e...

Process awareness is an essential success factor in any type of business. Process mining uses event data to discover and analyze actual business processes. Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficiently addressed the privacy concerns in this disciplin...

Initially, the focus of process mining was on processes evolving around a single type of objects, e.g., orders, order lines, payments, deliveries, or customers. In this simplified setting, each event refers to precisely one object and the automatically discovered process models describe the lifecycles of the selected objects. Dozens of process-disc...

Process mining techniques are widely used to uncover performance and compliance problems. However, the traditional focus on a single object type (i.e., case) is a limiting factor when considering real-life information systems. Therefore, there is an increased interest in object-centric process mining. This paper proposes a graph-based approach for...

When multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including at...

SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes that are contained in a specific SAP instance, and the creation of a list of related tables is a significant chal...

In spite of recent advances in process mining, making this new technology accessible to non-technical users remains a challenge. Process maps and dashboards still seem to frighten many line of business professionals. In order to democratize this technology, we propose a natural language querying interface that allows non-technical users to retrieve...

Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associated with multiple objects expressing the interaction of multiple processes. As traditional process mining techniques assume events...

Event logs, as viewed in process mining, contain event data describing the execution of operational processes. Most process mining techniques take an event log as input and generate insights about the underlying process by analyzing the data provided. Consequently, handling large volumes of event data is essential to apply process mining successful...