Process Mining Powered By Nokia
Objectives
Business processes are a primary asset of companies as they directly impact the products and services' success and perceived value. This course aims at developing knowledge and skills related to business process management through a data mining perspective. The process mining course has the goal to provide a theoretical and technical understanding of the optimization of business processes. It focuses on the application of conceptual methods and software tools to design, analyze, transform, monitor, and control business processes and improve their performance using the data from organizations.
General characterization
Code
200296
Credits
7.5
Responsible teacher
Frederico Miguel Campos Cruz Ribeiro de Jesus
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
na
Bibliography
Wil van der Aalst (2016). Process Mining: Data Science in Action 2 Edition. Springer Berlin Heidelberg;
Dumas, M., Rosa, M. L., Mendling, J., & Reijers, H. A. (2018). Fundamentals of Business Process Management 2 Edition. Springer Berlin Heidelberg;
Michael Glykas (Ed.) (2013). Business Process Management: Theory and Applications. Springer Berlin Heidelberg;
Celonis® Process Mining Experts Program for Students.
Teaching method
Teaching based on lectures and practical classes. The lectures are expository sessions, which serve to introduce the fundamental concepts of Process Mining associated with each of the topics. The practical classes are based on the analysis, design and implementation of theoretical concepts, using a specific software.
Evaluation method
Evaluation
1 Term: Exam (60%) + Group Project (40%)
2 Term: Exam (60%) + Group Project (40%)
*Minimum grade for exam and group project is eight out of 20 points.
Subject matter
PROGRAM
Chapter | Content |
Chapter 1 – Introduction to BPM | 1. What is a business process? 2. The BPM Lifecycle 3. Modeling 4. BPM vs Process Mining |
Exercises | Exercises |
Chapter 2 – Introduction to Process Mining | 1. Introduction to Process Mining 2. The Process Mining Methodology 3. Process Mining Techniques 4. Petri nets 5. alfa algorithm 6. Four Competing Quality Criteria 7. The value of Process Mining 8. Use cases |
Exercises | Exercises |
Chapter 3 – Extraction, Transformation and Loading (ETL process) | 1. Basics of Data Integration 2. Data Pipeline |
Chapter 4 – Automated Process Discovery | 1. Variant, Process and Case Explorer 2. Filtering analysis |
Chapter 5 – Conformance Checking | 1. Automatic process insights 2. Conformance Checking Configuration |
Chapter 6.1 – Build quick analysis | 1. Tables and Charts Configuration 2. KPIs Creation |
Chapter 6.2 – Build analysis with PQL | 1. PQL language |
Chapter 7 - RPA | 1. Action Flows |
Chapter 7 – Predictive Process Mining | 1. Predictive Process Mining |
Project support | Project support |
EVALUATION
1 Term: Exam (60%) + Group Project (40%)
2 Term: Exam (60%) + Group Project (40%)
*Minimum grade for exam and group project is eight out of 20 points.
Programs
Programs where the course is taught:
- Specialization in Information Analysis and Management
- Specialization in Risk Analysis and Management
- Specialization in Business Intelligence
- Specialization in Information Systems
- specialization in Digital Transformation
- Specialization in Business Intelligence – Working Hours
- specialization in Information Systems - working hours
- PostGraduate in Information Analysis and Management
- PostGraduate Risk Analysis and Management
- PostGraduate in Business Intelligence
- PostGraduate in Data Science for Marketing
- PostGraduate in Digital Enterprise Management
- PostGraduate in Information Management and Business Intelligence in Healthcare
- PostGraduate Information Systems Management
- PostGraduate in Enterprise Information Systems
- Pós-Graduação em Transformação Digital