Duplicated Program

Registration

Super C, Templergraben 57, 52062 Aachen

Introduction Process Mining: A 360 Degree Overview

Presenter: Josep Carmona, Polytechnic University of Catalonia, Spain standing in for Wil van der Aalst, RWTH Aachen University, Germany

Process Discovery I: Foundations of Process Discovery

Presenter: Wil van der Aalst, RWTH Aachen University, Germany

Process discovery is probably the most interesting, but also most challenging, process mining task. After introducing Directly-Follows Graph (DFGs) and basic filtering, two main approaches are presented: (1) bottom-up process discovery (e.g., Alpha algorithm) and (2) top-down process discovery (e.g., inductive mining).

Conformance Checking I

Presenter: Boudewijn van Dongen, Eindhoven University of Technology, The Netherlands

In this session, the students will get an introduction in the field of conformance checking. Conformance checking is the subfield of process mining that focusses on the relation between modeled and observed behavior. After a general introduction, students are introduced to the computations and foundational concepts of token-based replay and alignments as a means to quantify the relationship between an event log to a model. Using token-based replay and alignments, several fitness metrics, both on trace and log level are introduced. Throughout the session a real-life like example is used to illustrate the concepts and at the end of the first session students should be able to manually compute both trace-based and log-based fitness using token replay or alignments for small example log and models.

Process Discovery III: Declarative Process Mining

Presenters: Claudio Di Ciccio, Sapienza University of Rome, Italy & Marco Montali, Free University of Bozen-Bolzano, Italy

We overview fifteen years of research on declarative process mining, that is, process mining for declarative process specifications. The declarative specification of processes is based on the elicitation of behavioral rules that constrain process executions. We focus on three fundamental tasks: reasoning, discovery, and monitoring.

Process Discovery II: Advanced Process Discovery Techniques

Presenter: Eric Verbeek, Eindhoven University of Technology, The Netherlands & Josep Carmona, Polytechnic University of Catalonia, Spain

Many state-of-the-art automated process discovery techniques, especially the basic ones, struggle to systematically discover accurate and simple process models. In general, as the complexity of the input data increases, the quality of the discovered process models can worsen quickly. Given that oftentimes real-life event logs record very complex and unstructured process behavior containing many repetitions, infrequent traces, and incomplete data, some state-of-the-art techniques turn unreliable and not purposeful. In this presentation, we introduce four advanced process discovery techniques that can be used to overcome the limitations of basic process discovery techniques in the aforementioned scenarios.

Conformance Checking II

Presenter: Boudewijn van Dongen, Eindhoven University of Technology, The Netherlands

In this advanced session, the students take a closer look into alignments. Using some Petri net theory, we see how alignments can be computed on real models and how the A* algorithm works that is implemented in ProM and many other tools. We then move on to a brief discussion on other conformance metrics beyond fitness, such as precision and generalization, but more importantly, we discuss conformance between a log and a model in the context of a process.  We conclude the session with a real example showing true conformance issues with implications in real life.

Data Preprocessing I: Methods and Techniques for Event Log Preparation

Presenter: Jochen De Weerdt, KU Leuven, Belgium

In this session, we will take a deep dive into the topic of preprocessing of event data. While strongly underexposed in research, data preparation in any process mining project will typically require up to 80% of the efforts. As such, significant opportunities exist to research and develop better methods and techniques. We will discuss (1) structure and extraction of event data, (2) event correlation and abstraction, (3) data quality, and (4) more exotic preprocessing techniques such as trace clustering and autoencoders.

Process Enhancement I: Foundations of Process Enhancement

Presenter: Massimiliano de Leoni, University of Padua, Italy

Process models have a descriptive or prescriptive nature to respectively discuss them with stakeholders, or to define how process’ instances must be executed. Models thus need to (1) precisely describe what allowed and disallowed, and (2) only enable behaviour that ensures better process’ outcomes and higher normative compliance. This talk introduces techniques to enhance models and achieve the two goals.

Predictive Process Monitoring

Presenters: Chiara Ghidini & Chiara Di Francescomarino, Fondazione Bruno Kessler, Italy

Predictive process monitoring is a branch of process mining that aims at predicting at runtime and as early as possible the future development of ongoing cases of a process given their uncompleted traces. This lecture aims at (i) offering an introduction on Predictive Process Monitoring, including an overview of the existing families of approaches and typical encodings; (ii) introducing one of the existing Predictive Process Monitoring tools.

Process Mining in a Streaming Setting: Discovery & Conformance

Presenter: Andrea Burattin, Technical University of Denmark

Streaming process mining refers to the set of techniques and tools which have the goal of processing a stream of events (as opposed to a finite event log). The goal of these module is to understand what is streaming process mining, why it is relevant and how it can be performed. Some general techniques will be presented.

Responsible Process Mining

Presenter: Felix Mannhardt, Process Analytics Group, Eindhoven University of Technology, The Netherlands

Responsible data science aims for responsibility to be built, by design, into management, analysis, and algorithmic decision-making techniques based on data to prevent harm. Process mining may also lead to harm. Participants learn about state of the art in Responsible Process Mining, which aims to systematically address issues from the perspectives of Fairness, Accuracy, Confidentiality, and Transparency (FACT).

Status and Future of Process Mining: from Process Discovery to Process Executions

Presenter: Dr Lars Reinkemeyer, VP Customer Transformation, Celonis SE

This sessions’ focus is on industry experience and operational best practices: 1. Siemens’ journey from leveraging Process Mining for Process Discovery to intelligent Process Execution. 2. The importance of balancing between data discovery and operational action. 3. Best practices in respect to business value realisation including a future outlook.

Applications I: Process Mining in Healthcare

Presenters: Niels Martin, Research Group Business Informatics, Hasselt University, Belgium & Nils Wittig, Chief Experience Officer at KMS Vertrieb und Services AG, Germany

Healthcare is a promising domain for process mining given the significant societal value that can be generated by supporting data-driven process improvement. In this lecture, you will be introduced to process mining in healthcare. First, the particularities of healthcare processes will be introduced, as well as ongoing research. Afterwards, we will take a deep dive into a real-life case study of process mining in healthcare. To end the lecture, some open challenges will be discussed.

Applications II: Using Process Mining for Financial Auditing

Presenter: Mieke Jans, Hasselt University, Belgium

To conclude the summer school, we will dive into a specific application domain where process mining holds great potential: financial auditing. In this session, an introduction will be given to the auditing environment (what is a financial audit, what is the difference between an internal and an external audit, how is an audit typically conducted…?). Next, you will learn how the main process mining techniques can be used throughout an audit engagement and how this can potentially change the audit profession.

Applications III: Robotic Process Automation

Presenter: Marlon Dumas, University of Tartu and Apromore, Estonia

In this lecture, you will learn about different techniques to analyze User Interaction (UI) logs to: (i) understand the differences in performance between workers or teams; and (ii) to discover repetitive routines that can be automated using robotic process automation, with an emphasis on routines for data transfer across software applications.