Algorithms for Massive Datasets
A.Y. 2026/2027
Learning objectives
The course aims at describing the big data processing framework, both in terms of methodologies and technologies.
Expected learning outcomes
Students:
- will be able to use technologies for the distributed storage of datasets;
- will know the map-reduce distributed processing framework and its leading extensions;
- will know the principal algorithms used in order to deal with classical big data problems, as well as to implement them using a distributed processing framework;
- will be able to choose appropriate methods for solving big data problems.
- will be able to use technologies for the distributed storage of datasets;
- will know the map-reduce distributed processing framework and its leading extensions;
- will know the principal algorithms used in order to deal with classical big data problems, as well as to implement them using a distributed processing framework;
- will be able to choose appropriate methods for solving big data problems.
Lesson period: Second four month period
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
INFO-01/A - Informatics - University credits: 6
Lessons: 48 hours