Identifying Data 2023/24
Subject (*) Process Mining Code 614544025
Study programme
Máster Universitario en Intelixencia Artificial
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
 First Optional 3
Language
English
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Coordinador
Varela Rodeiro, Tirso
 E-mail
tirso.varela.rodeiro@udc.es
Lecturers
Varela Rodeiro, Tirso
 E-mail
tirso.varela.rodeiro@udc.es
Web http://https://moodle.udc.es/
General description O obxectivo da materia é proporcionar os conceptos teóricos e as habilidades prácticas para o desenvolvemento de técnicas intelixentes no ámbito da información proporcionada pola execución dos procesos de negocio co fin de mellorar e optimizar o seu rendemento. A materia abordarase desde un enfoque descritivo, no que se introducirán técnicas que permiten coñecer o que sucedeu e non o que se pensa que sucede; e preditivo, no que se presentarán os principais retos da monitorización preditiva e as técnicas intelixentes que dan resposta a ditos retos.

Study programme competencies
Code Study programme competences
A8 CE07 - Ability to understand the consequences of the development of an explainable and interpretable intelligent system
A9 CE08 - Ability to design and develop secure intelligent systems, in terms of integrity, confidentiality and robustness
A10 CE09 - Ability to obtain a deep knowledge about fundamental principles and models of quantum computing and to apply them for the interpretation, selection, evaluation, modelling and creation of new concepts, theories, uses and technological developments related to Artificial Intelligence
A12 CE11 - Understanding and command of the main techniques and tools for data analysis, both from the statistical and the machine learning viewpoints, including those devised for large volumes of data, and ability to select those ones most suitable for problem solving
A14 CE13 - Knowledge of computer tools in the field of data analysis and statistical modelling and ability to select those ones most suitable for problem solving
A15 CE14 - Understanding and command of the main machine learning techniques, including those devised for big volumes of data. Understanding and command of basic concepts and techniques for information search and filtering in big collections of data.
A16 CE15 - Knowledge of computer tools in the field of machine learning and ability to select those ones most suitable for problem solving
A17 CE16 - Knowledge of the process and tools for processing and preparing data, from their acquisition, extraction, and cleansing to their transformation, loading, organisation and access
A20 CE19 - Knowledge of the different environments where AI based technologies can be applied and awareness of their capability to provide a differentiating added value
A21 CE20 - Ability to combine and adapt different techniques, extrapolating knowledge among different application domains
A22 CE21 - Knowledge of the techniques that facilitate the efficient organisation and management of AI projects in real environments, including resources management and tasks scheduling and taking into account the concepts of knowledge dissemination and open science
A23 CE22 - Knowledge of the techniques that facilitate the security of data, applications and communications and the derived consequences on different application domains in AI
A28 CE27 - Understanding the significance of the entrepreneurial culture and knowledge of the resources within the enterpreneur person's means
A29 CE28 - Appropriate knowledge of the concept of enterprise, its organisation and management, and of the different business sectors, with the goal of providing solutions from the AI perspective
A30 CE29 - Being able to apply knowledge, abilities and attitudes to the business and professional world, by planning, managing and evaluating projects in the scope of AI
A31 CE30 - Being able to set out, model and solve problems that require the application of AI methods, techniques and technologies
B1 CG01 - Maintaining and extending theoretical foundations to allow the introduction and exploitation of new and advanced technologies in the field of AI
B2 CG02 - Successfully addressing each and every stage of an AI project
B4 CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field
B5 CG05 - Working in teams, especially of multidisciplinary nature, and being skilled in the management of time, people and decision making
B6 CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context
B7 CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study
B9 CB04 - The students will be able to communicate their conclusions, their premises and their ultimate justifications, both to specialised and non-specialised audiences, using a clear style language, free from ambiguities
B10 CB05 - The students will acquire learning abilities to allow them to continue studying in way that will mostly be self-directed or autonomous
C5 CT05 - Understanding the importance of the entrepreneurial culture and knowledge of the resources within the entrepreneur person's means
C8 CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society
C9 CT09 - Being able to manage time and resources: outlining plans, prioritising activities, identifying criticisms, fixing deadlines and sticking to them

Learning aims
Learning outcomes Study programme competences
Know the main process discovery techniques and be able to select the most appropriate for a given domain. AC7
AC13
AC14
AC16
AC28
 BC7
BC9
 CC5
CC9
Know how to apply search and optimization techniques to verify the conformity of a process. AC15
AC29
AC30
 BC9
 CC8
Know and develop solutions based on artificial intelligence for predictive monitoring. AC9
AC11
AC16
 BC4
BC7
BC10
 CC9
Understand and solve optimization problems in business processes. AC21
 BC5
BC9
 CC9
Know and understand the quality metrics of a process. AC11
AC22
 BC6
 CC5
Know the main problems that process mining solves. AC8
AC19
AC20
AC27
 BC1
BC2
BC6
BC7
BC10
 CC5
CC8

Contents
Topic Sub-topic
THEORY 1. Process concept.
2. Event registration.
3. Key business and process indicators.
4. Process discovery.
5. Compliance of processes.
6. Process analytics.
7. Predictive monitoring.
8. Optimization of processes.
PRACTICE 1. Log analysis.
2. Discovery and analysis of processes.
3. Process compliance.
4. Predictive monitoring and optimization.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Laboratory practice A9 A14 A15 A16 A20 A21 A31 A30 B2 B4 B5 B9 B10 C8 C9 11 25 36
Guest lecture / keynote speech A8 A10 A12 A17 A22 A23 A28 A29 B1 B6 B7 C5 10 25 35
 
Personalized attention 4 0 4
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Laboratory practice They are aimed at students acquiring skills in the implementation and use of process mining techniques. The student will be exposed to a project that must be resolved throughout the subject and whose resolution requires the understanding and application of the theoretical-practical contents included in the contents of the subject.

Therefore, in the laboratory practices a project-based learning methodology will be followed. Attendance at these laboratory practices is MANDATORY.
Guest lecture / keynote speech They 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 the projects that will be developed throughout the subject will be adressed, highlighting the problems of each of the aspects that the students should focus on.

Personalized attention
Methodologies
Laboratory practice
Description
The doubts that may arise as part of the development of the teaching activities of the subject will be addressed.

Assessment
Methodologies Competencies Description Qualification
Guest lecture / keynote speech A8 A10 A12 A17 A22 A23 A28 A29 B1 B6 B7 C5 Exam in which mastery of the theoretical aspects of process mining will be demonstrated. You will need to answer a set of questions about process concepts and the different types of process analytics. 40
Laboratory practice A9 A14 A15 A16 A20 A21 A31 A30 B2 B4 B5 B9 B10 C8 C9 Completion 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 throughout the subject. In practice classes, the students will have to solve the questions using the most appropriate techniques to obtain information about the process analytics. 60
 
Assessment comments

If the student sends the first installment of the project, it will be considered as presented in the subject.

SECOND OPPORTUNITY

The evaluation criteria of the theory and practice parts in the second opportunity will be exactly the same as for the ordinary opportunity. Therefore, in addition to passing the theory exam and the bulletins, 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

Attendance at the interactive sessions is mandatory because they address key concepts of the subject, and the control of this attendance is done through signature sheets that will 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.

PLAGIARISM DETECTION

In the case of fraudulent completion of exercises or tests, the provisions of the "Rules of assessment, review and claim of qualifications for two undergraduate and university master's degrees of the University of Coruña" will apply. Link: Normas_avaliacion_revision_reclamacion_consolidado_l.pdf_2063069239.pdf (udc.es)

Sources of information
Basic

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
Complementary


Recommendations
Subjects that it is recommended to have taken before

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments

Attendance at laboratory practices is mandatory, and furthermore, this participation should be active. In addition to this, additional time will be needed to work on the following aspects:

1. Autonomous study of the concepts of process mining. The time dedicated to this study not only includes 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 problems.

2. Project development. This time is necessary for the development of the project to be completed, beyond the progress that takes place in the practice sessions. During 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 time of practical work.


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.