Hasso-Plattner-InstitutSDG am HPI
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Business Process Intelligence (Sommersemester 2022)

Lecturer: Prof. Dr. Mathias Weske (Business Process Technology)
Tutors: Stephan Haarmann

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.04.2022 - 30.04.2022
  • Examination time §9 (4) BAMA-O: 24.06.2022
  • Teaching Form: Lecture
  • Enrolment Type: Compulsory Module
  • Course Language: English

Programs, Module Groups & Modules

IT-Systems Engineering MA
Data Engineering MA
Digital Health MA
  • SCAD: Scalable Computing and Algorithms for Digital Health
    • HPI-SCAD-C Concepts and Methods
  • SCAD: Scalable Computing and Algorithms for Digital Health
    • HPI-SCAD-T Technologies and Tools
  • SCAD: Scalable Computing and Algorithms for Digital Health
    • HPI-SCAD-S Specialization
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-S Concepts and Methods
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-T Technologies and Tools
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-S Specialization

Description

Business processes management is the prime method for managing the operations of an enterprise. Thereby, information systems are employed to implement, monitor, and improve processes.

In the lecture "Business Process Intelligence," you will learn about well-established techniques in business process management as well as the newest developments in research and academia. Topics include modeling processes and decisions, verification and compliance checking, and process mining:

  • Process models are a corner stone of business process management. You will learn how to create good process models in the BPMN language. Thereby, we do not only look at the process of one organization but also at the interplay of multiple organizations.
  • You will learn how to analyze processes to find flaws, inefficiencies, and to verify compliance against regulations such as laws and guidelines.
  • You will learn how to gain process-related insights from data by creating event logs and employing process mining techniques for discovery, conformance checking, and enrichment.

The lecture provides you with an overview of BPM. Afterwards, you'll be well-equipped to deepen your knowledge in specialized lectures, seminars, projects, or self study, to contribute to process management projects in industry, and to conduct first research.

The course will be organized using Moodle [1] (key: bpi2022). Please register both at the Studienreferat and in the Moodle course linked above. For the Moodle you need the credentials for the University of Potsdam. Please consider forwarding your University of Potsdam e-mails to your main address and/or checking them regularly.

[1]

https://moodle2.uni-potsdam.de/course/view.php?id=32482

Outline

  1. Introduction
    1. Motivation, characterization and goals
    2. Process models and process instances
    3. Resources and their allocation
    4. Architecture of  execution environments
  2. Process Orchestrations
    1. Business process diagrams
    2. Decisions in process models
    3. Petri nets and workflow nets
  3. Process Choreographies
    1. Modeling process choreographies
    2. Analyzing process choreographies
  4. Analysis of Business Processes
    1. From models to formal models
    2. Soundness properties
    3. Process compliance primer
  5. Process Mining
    1. Basics of process mining
    2. Event log generation
    3. Discovery and conformance checking

Literature

Further literature will be provided during the semester.

Learning

  • Lecture
  • Graded assignments
  • Literature

Examination

The lecture will be complemented with graded assignments. Students can work in groups of two. Furthermore, students are assigned to read and summarize one seminal research paper from the field of business process management. The written summary will be graded and contributes 33% to the final grade. A written exam at the end of the semester contributes 67% of the overall grade. To participate in the exam, students are required to hand in all assignments and to reach at least 50% of the achievable points in each of two consecutive assignments (first assignment does not count). To pass the class, both the written summary and the written exam need to be passed.

Dates

Please consult the schedule provided by the Studienreferat for the slots of the lecture.
The lecture starts in the week April 18 - 24.

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