Process mining aims to analyze the inconsistencies between transaction logs and predicted process models obtained from corporate information systems with data-based methods developed in process discovery, conformance control, and process model enrichment. Therefore, process mining can also be called data science for business processes.
Within the scope of this course, an overview of data and process mining, the basic features of business processes and process mining, a detailed look at process model types and process mining principles, process discovery methods and transaction log-process model conformity control, process model’s enrichment and improvement of these business processes.
Prerequisite(s)
Course Code Course Name…
Corequisite(s)
Course Code Course Name…
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Lecturer Dr. Eren Esgin
Course Assistant(s)
Schedule
This course is not offered in this semester.
Office Hour(s)
This course is not offered in this semester.
Teaching Methods and Techniques
Oral presentation and Discussion
Principle Sources
Wil M. P. van der Aalst, “Process Mining: Discovery, Conformance and Enhancement of Business Processes”, Springer, 2011
Other Sources
Slides and course notes
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction and Data Mining
Oral presentation
2. Week
Process Models and Process Discovery
Oral presentation
3. Week
Process Models and Process Discovery
Oral presentation
4. Week
Different Types of Process Models
Oral presentation
5. Week
Process Discovery Techniques and Conformance Checking
Oral presentation
6. Week
Process Discovery Techniques and Conformance Checking
Oral presentation
7. Week
Enrichment of Process Models
Oral presentation
8. Week
Midterm
9. Week
Enrichment of Process Models
Oral presentation
10. Week
Operational Support and Conclusion
Oral presentation
11. Week
Operational Support and Conclusion
Oral presentation
12. Week
Workshop on Process Mining Software (Celonis or ProM)
Oral presentation
13. Week
Workshop on Process Mining Software (Celonis or ProM)
Oral presentation
14. Week
Workshop on Process Mining Software (Celonis or ProM)
Oral presentation
15. Week
Project Presentation & Paper Submission
Discussion
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
26
Quizzes
12
24
Project(s)
1
35
Attendance
1
15
Program Outcomes
PO-1
Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2
Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3
Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4
Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5
Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6
Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7
Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8
Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9
Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10
Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11
Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes of engineering solutions.
Learning Outcomes
LO-1
Gaining a different understanding of business process intelligence techniques (especially process mining)
LO-2
Understanding the role of big data in today's businesses
LO-3
Ability to relate process mining techniques to research areas such as simulation, business intelligence, data mining, and machine learning
LO-4
To be able to apply basic process discovery techniques to learn process models from event logs taken from information systems
LO-5
Ability to apply basic conformance checking techniques to compare event logs and predicted process models
LO-6
Enriching the business process model discovered according to the insights extracted from the event log
LO-7
Reaching the understanding needed to initiate the process mining project and run it in a structured framework
