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Trends in BPM Research (Wintersemester 2022/2023)

Dozent: Prof. Dr. Mathias Weske (Business Process Technology) , Tom Lichtenstein (Business Process Technology)

Allgemeine Information

  • Semesterwochenstunden: 2
  • ECTS: 3
  • Benotet: Ja
  • Einschreibefrist: 01.10.2022 - 31.10.2022
  • Lehrform: Vorlesung
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-K Konzepte und Methoden
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-S Spezialisierung
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
Data Engineering MA
Digital Health MA
Software Systems Engineering MA

Beschreibung

Business Process Management (BPM) concerns concepts and technologies to discover, model, execute, monitor, analyze, and improve processes within or between organizations. The Business Process Technology (BPT) group is engaged in versatile sub-topics of BPM, such as case management and process mining. Related fields like natural language processing and robotic process automation are explored to exchange mutually beneficial concepts. Further, the challenges of interorganizational process interactions are addressed through process choreographies.

The objective of the course is to present current research trends in BPM in general and at our chair in particular. We provide insights into our daily work, during which you will also get to know techniques for approaching research life. This might support you in defining your research topics, e.g., for your Master's thesis.

Thus, this lecture is particularly interesting if you want to get to know or deepen your understanding of our chair and our projects.

Course Topics

Among others, the following topics will be discussed in this course.

Process Mining

Data is everywhere nowadays, and organizations are striving for better techniques and methods to gain value from data. But how can data be used to provide insight on day to day operational processes? Process Mining is the discipline bringing data analysis to BPM. Process mining provides techniques to discover and analyze processes based on the real behavior, recorded in information systems. However, to exploit process mining capabilities, many steps have to be taken, from extracting and preparing data to performing various analysis techniques, to deploying results for process improvement and facilitating decision-making. Each of these involved phases require efficient concepts as well as complementary technology. Process mining research concerns development and improvement of techniques to provide the right methods and tools for each step, ensuring the realization of valuable results. In this course, we will also provide insights on the application of process mining techniques in the medical domain using real healthcare data.

Natural Language Processing

Automated techniques for the analysis of business processes provide a wide range of valuable opportunities for organizations. Among others, they allow checking for business process compliance, to identify redundant activities within an organization, and to identify operational overlap between two business processes. Traditionally, the input to such techniques are process models. That is, these techniques build on the formally specified relationships between the activities of process models to perform their analyses. This, however, means that these analysis techniques cannot be applied to less structured forms of process documentation, such as textual process descriptions. Realizing that a considerable share of process documentation is based on natural language text in many organizations, this part of the lecture will focus on how Natural Language Processing techniques can be leveraged and applied in a process analysis context.

Robotic Process Automation

Today's business processes still involve repetitive and monotonous tasks done by humans, such as filling out forms or copying values between applications. The field of robotic process automation (RPA) aims to provide software technology that allows building and managing software robots that emulate human interactions with software. This provides organizations with the ability to automate repetitive tasks without having to integrate complex interfaces or change the applications they are already using. Automation with software robots further allows for faster and more consistent results compared to human actors. In this part of the lecture, we will introduce the field of robotic process automation and discuss current challenges in this area. In addition, we will take a look at semantic robotic process automation, which strives to facilitate the creation, modeling, and maintenance of software robots and improve their resilience to change.

Knowledge-intensive Processes

Business processes are usually assumed to follow highly structured procedures whose behavior can be predetermined. In reality, however, the execution of certain processes depends heavily on data- and knowledge-intensive decisions made by so-called knowledge workers. To support these knowledge-intensive processes, the case management approach has been proposed. The central challenge of case management is to support flexible knowledge work in a dynamic environment to achieve a goal. However, due to their emergent and unpredictable behavior, knowledge-intensive processes tend to be complex and therefore difficult to understand. To help knowledge workers to achieve the best possible outcome, techniques are needed to provide guidance about the possible paths of knowledge-intensive processes. In this part of the lecture, we will take a closer look at knowledge-intensive processes and case management. Furthermore, we will consider how to cope with the complexity of case management by providing guiding mechanisms for knowledge workers.

Process Choreographies

In today's networked economy, businesses and organizations increasingly interact with each other to achieve their goals. The collective interaction behavior executed by the individual business processes of collaborating organizations forms a process choreography that can be captured by various process choreography models. Since the outcome of each participant's process execution depends on seamless interaction with other participants, the interaction behavior must be carefully designed to avoid conflicts and their associated costs. However, in a flexible environment, the interaction behavior may be unpredictable to some extent and thus requires flexible design that may not be expressed by current choreography modelling languages. In this part of the lecture, we will focus on different choreography modelling concepts and explore the impact of process flexibility on process interactions and choreography modelling.

Voraussetzungen

There are no hard requirements for participating in this lecture, though some knowledge about modeling (e.g., POIS lecture) is helpful.
Necessary background knowledge will be conveyed during the semester as far as possible.

Literatur

  • Mathias Weske: Business Process Management: Concepts, Languages, Architectures. Springer-Verlag Berlin Heidelberg 2019.
  • More literature will be published during the lecture by the respective lecturers.

Lern- und Lehrformen

The lecture is expected to be held in presence, except for two sessions (see below) which will be covered by existing OpenHPI courses.
Each other session will be covered by a member of the Business Process Technology chair. In addition, three guest lectures will be given, including two lectures held by well-known external researchers in the field and one lecture held by an established industry partner (Appian) active in related research areas, highlighting the industrial facets of the research topics.

 

All slides can be found in the "Materials" directory (FG Business Process Technology > 2022-WiSe Trends in BPM Research).

Leistungserfassung

The examination will be conducted in the form of oral exams at the end of the semester after the lecture period.

Termine

Date Topic Lecturer
17.10. Course introduction Tom Lichtenstein
24.10. Business Process Management OpenHPI
31.10. No session (Holiday) ­­---
07.11. Process Mining OpenHPI
14.11. Process Mining in Healthcare Jonas Cremerius
21.11. Discovering Data Models from Event Logs Dorina Bano
28.11. Natural Language Processing Prof. Henrik Leopold
05.12. Challenges in Robotic Process Automation Maximilian Völker
12.12. End-to-End Process Automation Appian
19.12. No session (Holiday) ---
26.12. No session (Holiday) ---
02.01. Guiding Knowledge-intensive Processes Anjo Seidel
09.01. Data Execution Semantics Maximilian König
16.01. Flexible Process Choreographies Tom Lichtenstein
23.01. Sampling in Process Mining Prof. Matthias Weidlich
30.01. Taming the Complexity of Discovered Process Models Prof. Jan Mendling  
06.02. Course Summary Tom Lichtenstein
21.02. Exam day 1 ---
22.02. Exam day 2 ---

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