ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 4.5 Expository Class: 16 Interactive Classroom: 12 Total: 32.5
Use languages Spanish, Galician
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Architecture and Technology, Área externa M.U en Internet de las Cosas - IoT
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The ever-increasing amount of information accessible via the Internet makes the efficient processing of large amounts of data of increasing interest. This has led to the development of new techniques for storing and processing huge amounts of information, techniques that are naturally adapted to distributed systems.
The main objective is to identify the main Big Data architectures for IoT for Society 5.0/IIoT/Connected Vehicle applications and their data processing mechanisms, as well as the main statistical and storage/management techniques.
- Introduction to Big Data architectures for IoT in Society 5.0/IIoT/Connected Vehicle environments.
- Statistical techniques for large-scale IoT data in Society 5.0/IIoT/Connected Vehicle environments.
- Storage and task management in Big Data architectures for IoT in Society 5.0/IIoT/Connected Vehicle environments.
- Large-scale IoT data processing in Society 5.0 environments: batch processing and streaming/IIoT/Connected Vehicle.
Basic bibliography
- Class notes provided by the teacher
- T. White, Hadoop: The Definitive Guide, 4th Edition, O'Reilly, 2015
- B. Chambers, M. Zaharia, Spark: The Definitive Guide, O'Reilly, 2018
Complementary bibliography
- Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, Learning Spark. Lightning-Fast Big Data Analysis, O'Reilly, 2015
- Chuck Lam, Hadoop in Action, Manning, 2011
- Fabian Hueske, Vasiliki Kalavri, Stream Processing with Apache Flink", O'Reilly, 2019
Society 5.0
The student will be able to:
- Identify the main Big Data architectures for IoT for Society 5.0 applications and their data processing mechanisms, as well as the main statistical and storage/management techniques. (S-CN6).
- Apply statistical techniques to large-scale IoT datasets and for Society 5.0 applications.
- Design and deploy large-scale IoT data processing systems for Society 5.0 applications (S-CP4)).
Industrial IoT
The student will be able to:
- Know and understand the main Big Data architectures for IIoT and their data processing mechanisms, as well as the main statistical and storage/management techniques (I-CN1).
- Apply statistical techniques to large-scale IIoT data sets (I-HB1).
- Design and deploy large-scale IIoT data processing systems. (I-CP1)
Connected Vehicle
The student will be able to:
- Know and understand the main Big Data architectures for connected vehicle applications and their data processing mechanisms, as well as the main statistical and storage/management techniques. (V-CN1)
- Apply statistical techniques to large-scale data in IoT applications of the connected vehicle (V-HB1).
- Design and deploy large-scale IoT data processing systems for connected vehicle applications. (V-CP3)
Competences of the degree that are worked on (see degree report):
- Compulsory: CMP6, CMP11, CMP13.
- Specific: S-CP4, I-CP1, V-CP3.
- Theoretical classes, in which the content of each topic is exposed. The student will have copies of the transparencies beforehand and the teacher will promote an active attitude, asking questions to clarify specific aspects and leaving open questions for the student's reflection.
- Practical classes in the computer classroom, which allow the student to familiarize from a practical point of view with the questions exposed in the theoretical classes.
Classroom training activities and their relation with the competencies of the degree program
- Theoretical classes: given by the professor and seminar exposition. Worked competences: CMP6, S-CP4, I-CP1, V-CP3.
- Practical laboratory classes, problem-solving, and case studies. Worked competences: CMP6, S-CP4, I-CP1, V-CP3.
Non-attendance training activities and their relation with the competencies of the degree:
- Personal work of the student: bibliography consultation, autonomous study, development of programmed activities, preparation of presentations and works. Worked competences: CMP11, CMP13, CMP6, S-CP4, I-CP1, V-CP3.
Ordinary opportunity:
Contribution to final grade and evaluation criteria:
- Final exam: 40%.
In this part, the competencies CMP6, S-CP4, I-CP1, and V-CP3 will be evaluated implicitly or explicitly.
- Evaluation of practical work: 40% of the grade
The students will approach the resolution of diverse problems proposed in the computer classroom. They will have to carry out works in which the obtained results will be presented. Several of these works will be of obligatory delivery and others optional, which will allow to raise the grade. All the works will have to be delivered before the dates that will be specified, and they will have to fulfill some minimum requirements of quality to be taken into consideration. The degree of compliance with the specifications, the methodology and rigorousness and the presentation of results will be evaluated. In this part, the competencies CMP6, S-CP4, I-CP1, and V-CP3 will be evaluated implicitly or explicitly.
- Evaluation of tutored work: 20% Competence evaluated CMP11, CMP13, CMP6, S-CP4, I-CP1, V-CP3.
To pass the subject, a total score of 5 or higher must be achieved. It is also essential to have handed in all the compulsory practices.
Condition for qualification of Not Presented: not to present any practice.
Students who are not newly enrolled do not retain grades from previous courses.
Recovery opportunity (July) and extraordinary:
The evaluation will be the same as in the ordinary opportunity. Students who did not submit the proposed assignments throughout the term must submit them before the established date.
Condition for qualification of Not Presented: not submitting any practice.
In the case of fraudulent performance of exercises or tests, the regulations of the Normativa de avaliación do rendemento académico dos estudantes e de revisión de cualificacións will be applied.
In the application of the Normativa da ETSE sobre plaxio (approved by the ETSE Council on 12/19/2019), the total or partial copy of any practical ot theory exercise will mean failure on both opportunities of the course, with a grade of 0.0 in both cases.
- Blackboard classes: 10 hours face-to-face + 16 hours of independent student work
- Practical classes: 10 hours in class + 28 hours of autonomous work of the student.
- Tutorials and evaluation activities: 4 hours face-to-face + 7 hours of student's autonomous work.
- Total: 75 h
Due to the strong interrelation between the theoretical and practical parts and the progressive presentation of closely related concepts in the theoretical part, it is advisable to dedicate a daily study or review time.
- Classes are taught in Spanish. Videoconferencing, chat, etc.: intensive use of online communication tools will be made
The software tools used in this subject are open source.
Anselmo Tomás Fernández Pena
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Architecture and Technology
- Phone
- 881816439
- tf.pena [at] usc.es
- Category
- Professor: University Professor
| Thursday | |||
|---|---|---|---|
| 17:00-18:30 | Grupo /CLE_01 | Spanish | Aula A10 |
| 18:30-20:00 | Grupo /CLIL_01 | Spanish | Aula A10 |