ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 11 Total: 22
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Science and Artificial Intelligence, Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The objective of the subject is to provide the theoretical concepts and practical skills for the development of intelligent techniques in the field of information provided by the execution of business processes, in order to improve and optimize their performance. The subject will be approached from a descriptive approach, in which the techniques that allow knowing what has happened and not what is believed to happen will be introduced, and predictive, in which the main challenges of predictive monitoring and the intelligent techniques that respond to these challenges will be presented.
THEORY
1. Concept of process.
2. Recording of events.
3. Key business and process indicators.
4. Process discovery.
5. Conformance checking.
6. Process analytics.
7. Predictive monitoring.
8. Process optimization.
PRACTICE
1. Log analysis.
2. Process discovery.
3. Conformance checking.
4. Predictive monitoring and optimization.
BIBLIOGRAPHY (in order of priority)
1. VAN DER AALST, Wil. Process Mining - Data Science in Action. 2a Edición, Springer 2016. ISBN 978-3-662-49850-7.
2. FLUXICON. https://fluxicon.com/book/read/
3. FERREIRA, Diogo R. A primer on process mining: Practical skills with Python and Graphviz. 2a Edición, Springer 2020. ISBN: 978-3-030-41818-2
The students will acquire a set of specific competences of process mining, but also a series of basic and generic competences that are common to every development of a system based on Artificial Intelligence and, finally, some transversal competences that affect the personal skills of the students and the way in which they relate to other students. Taking this into account, the competences are the following:
BASIC
CB1 - To possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
CB2 - That students know how to apply the acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.
CB4 - That students know how to communicate their conclusions -and the knowledge and ultimate reasons that support them- to specialized and non-specialized audiences in a clear and unambiguous way.
CB5 - That students possess the learning skills that will allow them to continue studying in a way that will be largely self-directed or autonomous.
GENERAL
CG1 - Maintain and extend sound theoretical approaches to enable the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
GC2 - Successfully address all stages of an Artificial Intelligence project.
GC4 - To elaborate adequately and with some originality written compositions or motivated arguments, to write plans, work projects, scientific articles and to formulate reasonable hypotheses in the field.
CG5 - Work in teams, especially multidisciplinary teams, and be skilled in time management, people and decision making.
TRANSVERSALS
CT5 - Understand the importance of entrepreneurial culture and know the means available to entrepreneurs.
CT8 - Value the importance of research, innovation and technological development in the socioeconomic and cultural progress of society.
CT9 - Have the ability to manage time and resources: develop plans, prioritize activities, identify critical ones, set deadlines and meet them.
SPECIFIC
CE11 - Understanding and mastery of the main data analysis techniques and tools, both from a statistical and machine learning point of view, including those dedicated to the processing of large volumes of data, and the ability to select the most appropriate for problem solving.
CE16 - Knowledge of the process and tools for data processing and preparation from data acquisition or extraction, cleaning, transformation, loading, organization and access.
The teaching methodology is aimed at focusing on the practical aspects of process mining and on the concepts that differentiate it from the more classical data mining techniques. A distinction is made between lectures and laboratory practices:
(1) Master classes (10 hours) are aimed at explaining the concepts and characteristics of process mining, with special emphasis on the type of problems it solves and the different kinds of techniques that could be applied to solve each of them. In addition, in these classes reference will be made to the project that will be developed throughout the subject, highlighting the problematic of each of the aspects that will have to be addressed to solve them.
(2) Laboratory activities (11 hours) are aimed at students acquiring skills in the implementation and use of process mining techniques. Students will be presented with a project to be developed throughout the course, the resolution of which requires the understanding and application of the theoretical and practical contents included in the course contents. Therefore, the laboratory activities will follow a project-based learning methodology. Attendance to these laboratory activities is MANDATORY.
The evaluation of the subject will take place in two different, yet complementary, ways that aim to assess competence in the process mining domain. On the other hand, a distinction will be made between the evaluation of the ordinary opportunity and the recovery one:
ORDINARY OPPORTUNITY
(1) Examination in which the mastery of the theoretical aspects of process mining will be demonstrated. A set of questions on process concepts and different types of process analytics must be answered. This part will represent 40% of the final grade of the subject.
(2) Realization of a project that will start from the explanation of the process and the data that are the input to the process mining techniques and that the students will develop along the course. In the practicals, the students will have to solve the questions posed at each moment, using the most appropriate techniques to obtain information about the process analytics. This part will constitute 60% of the final grade of the subject.
Finally, if the student makes the first delivery of the project, it will be considered as presented in the subject.
OPPORTUNITY FOR RECOVERY
The evaluation criteria of the theory and practical parts in the recovery opportunity will be exactly the same as for the ordinary opportunity. Therefore, in addition to passing the theory exam and the reports, in order to pass the subject it will be necessary to have attended the interactive practical sessions (with the attendance criteria indicated below).
ATTENDANCE CONTROL
As mentioned above, the attendance to the interactive practical sessions is compulsory because they deal with key concepts of the subject, and the control of this attendance will be done through signature sheets that must be covered at the end of each of the sessions. In addition, if less than 80% of the interactive practical sessions are attended, it will be considered that the subject has not been passed.
In the case of fraudulent performance of exercises or tests, it will apply the provisions contained in the rules of evaluation of the academic performance of students and review of grades (https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…). In application of the regulations of the ETSE on plagiarism (approved by the Xunta da ETSE on 19/12/2019), the total or partial copy of any exercise of practices or theory will mean the failure of the two opportunities of the course, with the qualification of 0.0 in both cases.
As indicated above, attendance at the laboratory activities is mandatory, and furthermore, such participation should be active in order to make good use of the time. In addition to this, additional time will be needed to work on the following aspects:
(1) Self-study of process mining concepts (10 hours). The time dedicated to this study includes not only the time needed to prepare for the theoretical exam, but also the time needed to understand the theoretical concepts so that they can be correctly applied to solve the problems.
(2) Complete the development of the project (55 hours). This time is necessary to complete the development of the project, beyond the progress that takes place in the practical sessions. In this time it will be possible to internalize the way to solve the exposed problem, to the extent that in the practical sessions more emphasis is placed on understanding the problem and the types of techniques that are necessary to address it, while the details necessary to complete the problem should be done in the additional practical work time.
In order to take advantage of the subject and acquire the concepts of process mining, it is highly advisable to take advantage of the theory classes and laboratory practices, since, as presented in the syllabus and in the teaching methodology, these activities are directly related between them. On the other hand, it is also highly recommended that students explore the support material (web pages on technology, online tutorials of development environments, description of success stories, etc.) which include additional explanations to those of the classroom classes and help to understand and strengthen the concepts of process mining.
The preferred language for this subject is English.
Manuel Lama Penin
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816427
- manuel.lama [at] usc.es
- Category
- Professor: University Professor
| Monday | |||
|---|---|---|---|
| 17:00-18:30 | Grupo /CLE_01 | English | IA.12 |
| 18:30-20:00 | Grupo /CLIL_01 | English | IA.12 |
| 06.03.2025 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 06.03.2025 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 07.10.2025 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 07.10.2025 16:00-20:00 | Grupo /CLIL_01 | IA.02 |