DATABASES
cod. 09178

Academic year 2024/25
2° year of course - First semester
Professor
Flavio BERTINI
Academic discipline
Informatica (INF/01)
Field
Discipline informatiche
Type of training activity
Characterising
72 hours
of face-to-face activities
9 credits
hub:
course unit
in ITALIAN

Learning objectives


At the end of the course the student:
- knows the relational data model ed the basic constructors of SQL;
- can design and develop a database;
- is capable of processing a project to implement and information system.

Taking Dublin Indicators into account:

Knowledge and understanding
The course introduces the first concepts related to the operating systems. Particular emphasis is given to the understanding of the main algorithm underlying prominent kernel tasks. The reference text is in Italian, but standard English terminology is commonly used during the lessons as goodwill to the consultation of the international scientific literature.

Applying knowledge and understanding
The knowledge presented is always applied to the resolution of specific problems. The companion exercises are focused on problem solving and testing the comprehension of proposed algorithms. Often the solution methods are presented in the form of an algorithm, providing the students the ability to formalize procedures that are useful in many parts of computer science, and not only in the study of operating systems.

Making judgments
The exercises, which are proposed in relation to the theoretical part presented in class, can be solved individually or in groups. The comparison with classmates, work at home or in classroom, favors the development of specific skills in students to enable the explanation of arguments to fellows and teachers. Often the exercises can be solved in many different ways and listening to the solutions proposed by other allows students to develop the ability to identify common structures, beyond the apparent superficial differences.

Communication skills
The numerous discussions on the different methods to solve problems allow students to improve communication skills. Specific communication of computer technology is also used during classes and exercises.

Learning skills
The study of the origins of technological solutions and their introduction motivated by quantitative considerations contributes to the students’ ability to learn in a comprehensive way. The knowledge acquired is never rigid and definitive, but it is adaptable to any evolution and change of perspective and context.

Prerequisites


The knowledge of basic programming languages concepts is mandatory. The knowledge of the basic concepts of operating systems and communication networks is also helpful (even though not strictly required).

Course unit content


- Databases
- Relational data model
- Relational algebra and calculus
- SQL
- Database design methodology
- ER data model and quality verification
- Laboratory: notions about the architecture of a DBMS, indexes, transactions and design examples

Full programme

Bibliography


Atzeni, P., Ceri, S., Fraternali, P., Paraboschi, S., & Torlone, R. (2023). Basi di dati, 6/ed, McGraw-Hill Education. ISBN: 978-8838656545.

Teaching methods


Lectures and guided exercises.

Assessment methods and criteria


The exam consists of a written test followed by an optional oral interview. It is possible to take the written test several times, but each attempt cancels the previous one.

The written exam consists of 4 sections:
- Multiple choice questions for the verification of general knowledge of the course subjects.
- Exercise requiring writing queries in relational algebra and SQL.
- Exercise requiring the design of an E.R. diagram starting from a requirements description.
- Exercise and/or open questions for the laboratory part of the course.

There will be a midterm test in November, which, if passed, allows exemption from part of the written test in January and February.

Other information

2030 agenda goals for sustainable development

Contacts

Toll-free number

800 904 084

Student registry office

E. segreteria.scienze@unipr.it
T. 0521 90 5116

Quality assurance office

Education manager
dr. Claudia Buga
T. 0521 90 2842
Office e-mail: smfi.didattica@unipr.it
Manager e-mail: claudia.buga@unipr.it

President of the degree course

Prof. Alessandro Dal Palù
E. alessandro.dalpalu@unipr.it

Faculty advisor

Prof. Vincenzo Arceri
E. vincenzo.arceri@unipr.it

Career guidance delegate

Prof. Roberto Alfieri
E. roberto.alfieri@unipr.it

Tutor Proffesors

Prof. Enea Zaffanella
E. enea.zaffanella@unipr.it

Erasmus Delegates

Prof. Roberto Bagnara
E. roberto.bagnara@unipr.it
Student tutor dr. Anna Macaluso
E. anna.macaluso@studenti.unipr.it

Quality assurance manager

Prof. Roberto Alfieri
E. roberto.alfieri@unipr.it

Internships

Prof. Roberto Alfieri
E. roberto.alfieri@unipr.it

Tutor students

Tutor a.a. 2021-2022 dr. Francesco Manfredi
E. francescosaverio.manfredi@studenti.unipr.it

Student representatives: 
Greta Dolcetti 
Massimo Frati
Davide Tarpini