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Trends in BPM Research (Wintersemester 2023/2024)

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

General Information

  • Weekly Hours: 2
  • Credits: 3
  • Graded: yes
  • Enrolment Deadline: 01.10.2023 - 31.10.2023
  • Examination time §9 (4) BAMA-O: 13.02.2024
  • Teaching Form: Lecture / Seminar
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English

Programs, Module Groups & Modules

IT-Systems Engineering MA
Data Engineering MA
Digital Health MA
Software Systems Engineering MA
  • HPI-SSE-S Systems Foundations
  • SSYS: Software Systems
    • HPI-SSYS-C Concepts and Methods
  • SSYS: Software Systems
    • HPI-SSYS-T Technologies and Tools
  • DSYS: Data-Driven Systems
    • HPI-DSYS-C Concepts and Methods
  • DSYS: Data-Driven Systems
    • HPI-DSYS-T Technologies and Tools
  • MODA: Models and Algorithms
    • HPI-MODA-C Concepts and Methods
  • MODA: Models and Algorithms
    • HPI-MODA-T Technologies and Tools

Description

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 robotic process automation or large language models 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.

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.

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. Yet, 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. In this part of the lecture, we will focus on different choreography modeling concepts and explore the impact of data on process interactions and choreography modeling.

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. 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.

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.

Enrollment

The course will be organized using Moodle [1] (key: bpmTrends2324). Please register both at the Studienreferat and in the Moodle course linked above.

[1] https://moodle.hpi.de/course/view.php?id=661

Requirements

This lecture is designed to explore advanced topics in BPM research. We assume that you have basic knowledge of BPM, e.g., from POIS (Process-oriented Information Systems) or BPI (Business Process Improvement) lectures.

If you have not had the opportunity to attend these lectures, or if you would like to refresh your understanding of BPM fundamentals, we recommend that you review the following openHPI lectures on BPM in advance:

Business Processes: Modeling, Simulation, Execution
Watchlist:

  • Week 1: 1.1-1.7
  • Week 2: 2.1-2.6

A Step-by-Step Introduction to Process Mining
Watchlist:

  • Week 1: 1.2-1.8

Literature

  • 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.

Learning

The lecture is expected to be held in presence.
Each session will be presented by a member of the Business Process Technology chair. In addition, five guest lectures will be given, including two lectures by well-known external researchers (Prof. Dr. Cesare Pautasso and Prof. Dr. Luise Pufahl) in the field and three lectures by established industrial partners active in related areas (Camunda, SAP Signavio, and mama health), to highlight the industrial facets of the research topics.

All material, including slides to each lecture, can be found in the Moodle.

Examination

The examination will take the form of an oral examination on the following dates:

  • Tuesday, 13.02.2024
  • Wednesday, 14.02.2024

In addition, there will be optional quizzes after each lecture, which can be found in the Moodle. The quizzes are not required to complete the course.

Dates

Date Topic Lecturer
16.10. Course Introduction Prof. Dr. Mathias Weske
& Tom Lichtenstein
23.10. Data Execution Semantics Maximilian König
30.10. Data in Process Choreographies Tom Lichtenstein
06.11. Research at BPT Prof. Dr. Mathias Weske
13.11. Guiding Knowledge-intensive Processes Anjo Seidel
20.11. Process Automation with Eventual Consistency
and At Least Once Semantics
Dr. Stephan Haarmann
(Camunda)
27.11. NLP and BPM Prof. Dr. Henrik Leopold
07.12. Design Patterns for Process-centric APIs Prof. Dr. Cesare Pautasso
11.12. Process Mining @SAP Signavio David Eickhoff
(SAP Signavio)
18.12. Quiz Discussion &
Intermediate Summary
Prof. Dr. Mathias Weske
& Tom Lichtenstein
25.12. No session (Holiday) ---
01.01. No session (Holiday) ---
08.01. Process Mining in Healthcare Jonas Cremerius
15.01. Challenges in RPA Maximilian Völker
22.01. Process Mining  for Patient Journeys Dr. Adriatik Nikaj
(Mama Health)
29.01. Business Process Simulation Prof. Dr. Luise Pufahl  
05.02. Quiz Discussion &
Course Summary
Prof. Dr. Mathias Weske
& Tom Lichtenstein
13.02. Exam day 1 ---
14.02. Exam day 2 ---

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