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.