Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Ingegneria e scienze informatiche - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
|---|
4 modules among the following2° Year activated in the A.Y. 2024/2025
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
4 modules among the following| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
2 modules among the following3 modules among the followingLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Big data (2023/2024)
Teaching code
4S008912
Teacher
Coordinator
Credits
6
Also offered in courses:
- AI & Cloud of the course Master's degree in Artificial intelligence
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Semester 1 dal Oct 2, 2023 al Jan 26, 2024.
Courses Single
Authorized
Learning objectives
The course aims to provide the fundamental concepts of distributed computing systems that deal with very large data sets, together with the programming paradigms adopted by these systems. At the end of the course the student must demonstrate that he has acquired the necessary knowledge to evaluate the possible alternatives in the design of the analysis of large amounts of data, considering the benefits and limitations of the different approaches. This knowledge will allow the student to: i) configure parallel data processing systems; ii) design solutions to analyze large amounts of data; iii) evaluate the solutions for data analysis with parallel systems, considering the system resources necessary for the analysis; iv) continue the studies autonomously in the development of advanced analysis of large amounts of data.
Prerequisites and basic notions
To take this course, it is necessary to know Java, while it is recommended to have some knowledge of Python, as well as elements of Computer Architectures
Program
* Programming frameworks:
-- Distributed filesystems (HDFS);
-- Data and graph processing (MapReduce, Pregel);
-- SQL-like systems (Pig, Hive);
-- NoSQL systems (HBase, Cassandra).
* Algorithms:
-- Design of algorithms for text processing;
-- Indexing algorithms (inverted indexing);
-- Graph analysis (PageRank).
* Datacenter architectures:
-- Datacenter organization;
-- Datacenter networking;
-- Failure management.
Bibliography
Didactic methods
Lectures in the classroom
Learning assessment procedures
Examination consists of a project and the corresponding documentation. The project aims at verifying the comprehension of course contents and the capability to apply these contents in the resolution of a problem.
Evaluation criteria
The project topic is agreed with the teacher and focus on specific case studies. The project includes the performance evaluation for different input sizes, and the evaluation of the implementation alternatives. After the evaluation of the project documentation, the student may give an oral exam where the details of the project are discussed.
Criteria for the composition of the final grade
The mark is given by the evaluation of both the material provided and the student's autonomy
Exam language
Italiano
