Studying at the University of Verona

A.A. 2019/2020

Academic calendar

Il calendario accademico riporta le scadenze, gli adempimenti e i periodi rilevanti per la componente studentesca, personale docente e personale dell'Università. Sono inoltre indicate le festività e le chiusure ufficiali dell'Ateneo.
L’anno accademico inizia il 1° ottobre e termina il 30 settembre dell'anno successivo.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates. .

Definition of lesson periods
Period From To
I semestre Oct 1, 2019 Jan 31, 2020
II semestre Mar 2, 2020 Jun 12, 2020
Exam sessions
Session From To
Sessione invernale d'esame Feb 3, 2020 Feb 28, 2020
Sessione estiva d'esame Jun 15, 2020 Jul 31, 2020
Sessione autunnale d'esame Sep 1, 2020 Sep 30, 2020
Degree sessions
Session From To
Sessione Estiva Jul 15, 2020 Jul 15, 2020
Sessione Autunnale Oct 16, 2020 Oct 16, 2020
Sessione Autunnale Dicembre Dec 11, 2020 Dec 11, 2020
Sessione Invernale Mar 17, 2021 Mar 17, 2021
Holidays
Period From To
Festa di Ognissanti Nov 1, 2019 Nov 1, 2019
Festa dell'Immacolata Dec 8, 2019 Dec 8, 2019
Vacanze di Natale Dec 23, 2019 Jan 6, 2020
Vacanze di Pasqua Apr 10, 2020 Apr 14, 2020
Festa della Liberazione Apr 25, 2020 Apr 25, 2020
Festa del lavoro May 1, 2020 May 1, 2020
Festa del Santo Patrono May 21, 2020 May 21, 2020
Festa della Repubblica Jun 2, 2020 Jun 2, 2020
Vacanze estive Aug 10, 2020 Aug 23, 2020

Exam calendar

The exam roll calls are centrally administered by the operational unit   Science and Engineering Teaching and Student Services Unit
Exam Session Calendar and Roll call enrolment   sistema ESSE3 . If you forget your password to the online services, please contact the technical office in your Faculty or to the service credential recovery .

Exam calendar

Per dubbi o domande Read the answers to the more serious and frequent questions - F.A.Q. Examination enrolment

Academic staff

B C D F G M O P Q S T U

Baruffi Maria Caterina

mariacaterina.baruffi@univr.it +39 045 8028827 - 47

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Bonacina Maria Paola

mariapaola.bonacina@univr.it +39 045 802 7046

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Carra Damiano

damiano.carra@univr.it +39 045 802 7059

Castellini Alberto

alberto.castellini@univr.it +39 045 802 7908

Combi Carlo

carlo.combi@univr.it 045 802 7985

Cristani Matteo

matteo.cristani@univr.it 045 802 7983

Cristani Marco

marco.cristani@univr.it +39 045 802 7841

Daffara Claudia

claudia.daffara@univr.it +39 045 802 7942

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Di Pierro Alessandra

alessandra.dipierro@univr.it +39 045 802 7971

Fiorini Paolo

paolo.fiorini@univr.it 045 802 7963

Fraccaroli Enrico

enrico.fraccaroli@univr.it 0458027048

Fummi Franco

franco.fummi@univr.it 045 802 7994

Geretti Luca

luca.geretti@univr.it +39 045 802 7850

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Giacobazzi Roberto

roberto.giacobazzi@univr.it +39 045 802 7995

Gregorio Enrico

Enrico.Gregorio@univr.it 045 802 7937

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Marzola Pasquina

pasquina.marzola@univr.it 045 802 7816 (ufficio); 045 802 7614 (laboratorio)

Mastroeni Isabella

isabella.mastroeni@univr.it +39 045 802 7089

Migliorini Sara

sara.migliorini@univr.it +39 045 802 7908

Muradore Riccardo

riccardo.muradore@univr.it +39 045 802 7835

Oliboni Barbara

barbara.oliboni@univr.it +39 045 802 7077

Posenato Roberto

roberto.posenato@univr.it +39 045 802 7967

Pravadelli Graziano

graziano.pravadelli@univr.it +39 045 802 7081

Quaglia Davide

davide.quaglia@univr.it +39 045 802 7811

Quintarelli Elisa

elisa.quintarelli@univr.it +39 045 802 7852

Segala Roberto

roberto.segala@univr.it 045 802 7997

Setti Francesco

francesco.setti@univr.it +39 045 802 7804

Spoto Nicola Fausto

fausto.spoto@univr.it +39 045 8027940

Storti Silvia Francesca

silviafrancesca.storti@univr.it +39 045 802 7908

Tomazzoli Claudio

claudio.tomazzoli@univr.it

Study Plan

The 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 enrolment year.

TeachingsCreditsTAFSSD
6
A
(MAT/02)
6
A
(FIS/01)
Lingua inglese competenza linguistica - liv. B1 (completo)
6
E
-
TeachingsCreditsTAFSSD
12
B
(INF/01)
6
C
(FIS/01)
6
B
(ING-INF/05)
6
C
(ING-INF/04)
12
B
(ING-INF/05)
TeachingsCreditsTAFSSD
12
B
(ING-INF/05)
1 module to be chosen among the following
6
C
(INF/01)
6
C
(ING-INF/04)
Tirocinio
6
F
-
Prova finale
6
E
-

1° Anno

TeachingsCreditsTAFSSD
6
A
(MAT/02)
6
A
(FIS/01)
Lingua inglese competenza linguistica - liv. B1 (completo)
6
E
-

2° Anno

TeachingsCreditsTAFSSD
12
B
(INF/01)
6
C
(FIS/01)
6
B
(ING-INF/05)
6
C
(ING-INF/04)
12
B
(ING-INF/05)

3° Anno

TeachingsCreditsTAFSSD
12
B
(ING-INF/05)
1 module to be chosen among the following
6
C
(INF/01)
6
C
(ING-INF/04)
Tirocinio
6
F
-
Prova finale
6
E
-

Legend | 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.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S00037

Credits

12

Coordinatore

Alberto Belussi

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Language of instruction

Italian

The teaching is organized as follows:

Teoria

Credits

6

Period

I semestre

Academic staff

Alberto Belussi

Tecnologie per le basi di dati

Credits

3

Period

II semestre

Academic staff

Alberto Belussi

Laboratorio

Credits

3

Period

II semestre

Academic staff

Roberto Posenato

Learning outcomes

The course aims to provide the necessary skills for:
(i) the design of data according to the requirements with reference to different application contexts and within the production process of software systems;
(ii) effective and efficient management and use of data;
(iii) the use of a system for the management of relational databases in order to create, manage and query databases;
(iv) the interaction of applications with relational databases.

At the end of the course the student must demonstrate knowledge and understanding of data models and query languages ​​that characterize systems for data management and knowledge of the fundamental mechanisms to develop applications that interact with a database; have the ability to apply the acquired knowledge and understanding skills for the design of a data collection in an effective way compared to a set of application requirements, the ability to query and efficiently use the data managed by a system, the ability to design and implement an application that interacts with a database; know how to develop the skills necessary to continue the studies independently in the field of data management systems and software solutions development.

Program

The program is specified for each module as follows.

Module of Theory
--------------------------
- Introduction to database management systems (DBMS): architectures and functionalities of a DBMS. Physical and logical data independence. Data models. Concepts of model, schema and instance of a database. Languages for database systems. DBMS vs. file system.
- Conceptual database design: conceptual data models. The Entity-Relationship model (ER). Elements of the ER model: entities, attributes, relationships, ISA hierarchies and cardinality constraints.
- Logical database design: logical data models, the relational data model. Elements of the relational data models: relations and integrity constraints. Mapping between conceptual schemas in ER model and logical schema in the relational model.
- Interacting with a database system: languages for the definition, querying and update of a database. The relational algebra. Optimization of algebraic expressions. SQL: select-from-where statement, join in SQL, the GROUP BY and ORDER BY clauses, using subqueries. Views.

Teaching methods: lecturing, practicing with the teacher, didactical material (slides) and further exercises available on the eLearning platform, individual meetings during office hours according to the timetable published on the teacher web page.

Module of Lab
---------------------
- Introduction to the relational database management system (RDBMS) PostgreSQL.
- Introduction to the use of SQL in PostgreSQL.
- Query Optimization.
- Introduction to the transaction.
- Introduction to Python Language.
- Database access from applications written in Java/Python.

Lecturing and practicing in computer laboratory, didactical material (slides) and further exercise texts are available on the eLearning platform, the teacher is available for individual meeting in office hours.

Module of Database technology
----------------------------------------------
- The internal architecture of a DBMS. Transactions. Transactions properties. The concurrency control: schedules, the two-phase locking. Access methods (indexes): primary and secondary indexes, B-+tree, hashing based access methods. Query execution and optimization.
- Techniques for the interaction between a DBMS and an application.
- XML, XML schema, UML for XML data design (hints).

Teaching methods: lecturing, practicing with the teacher, didactical material (slides) and further exercises available on the eLearning platform, individual meetings during office hours according to the timetable published on the teacher web page.

Examination Methods

The exam is composed of two parts: theory and laboratory.

To pass the exam, the student must show that:
- they have understood the concepts related to the theory of relational databases and their design and implementation on database management systems
- they are able to describe the concepts in a clear and exhaustive way without digressions
- they are able to apply the acquired knowledge to solve application scenarios described by means of exercises, questions and projects.

Module of Theory and Database technology
----------------------------------------------------------------
For the modules of "Teoria" and "Tecnologie per le basi di dati" the exam consists of a written test with a duration of 2.5 hours containing: (i) an exercise about the conceptual modeling (using the E-R model) and the logical modeling (using the relational model) of a database; (ii) some exercises about the specification of queries in relational algebra on a given database; (iii) some exercises on XML and XML-Schema and some questions on the theory. On the e-learning platform in the section "TEMI D'ESAME E ALTRI ESERCIZI RIEPILOGATIVI" some tests of the previous years are published.
During the year, it is also possible to undergo the mid-term tests: these tests are fixed by the teacher in agreement with the students and are managed on the eLearning platform. These are three tests: the first test regards the design of a relational database: conceptual design (E-R model) and logic design (relational model), this weighs 4/9 of the theory grade; the second test regards the specification of queries on a relational database in relational algebra and SQL, this test weighs 3/9 of the theory grade; the third test on the program module of Databases Technologies, the latter test weighs 2/9 of the theory grade.

Module of Laboratorio
---------------------------------
The examination consists of a written test containing 5 exercises based on the module program and of an oral examination where it may be required to resolve questions using the computer.
A student who obtains less than 13/30 in a written exam, will also have to take an oral examination once it has passed the written exam obtaining 18/30 at least. The final grade will be the average of two grades. The grade in this module is worth 1/4 of the grade in the course examination. A selection of previous exam tests is published at http://profs.scienze.univr.it/~posenato/courses/labBD/raccoltaTemiEsameLaboratorioBasiDatiDal2016.pdf

The total grade (theory+laboratory) is given by the following weighted average: theory_grade*3/4 + laboratory_grade*1/4.

Bibliografia

Reference texts
Activity Author Title Publishing house Year ISBN Notes
Teoria Paolo Atzeni, Stefano Ceri, Piero Fraternali, Stefano Paraboschi, Riccardo Torlone Basi di dati (Edizione 5) McGraw Hill 2018 9788838694455
Tecnologie per le basi di dati Paolo Atzeni, Stefano Ceri, Piero Fraternali, Stefano Paraboschi, Riccardo Torlone Basi di dati (Edizione 5) McGraw Hill 2018 9788838694455
Laboratorio Docente del corso Dispense del docente 2020
Laboratorio Autori Vari Manuale di Postgresql (https://www.postgresql.org/docs/) Postgresql  

Tipologia di Attività formativa D e F

Academic year
1° periodo di lezioni From 9/30/19 To 12/14/19
years Teachings TAF Teacher
The fashion lab (1 ECTS) D Maria Caterina Baruffi (Coordinatore)
I semestre From 10/1/19 To 1/31/20
years Teachings TAF Teacher
Control theory D Riccardo Muradore (Coordinatore)
Biomedical Data and Signal Processing D Silvia Francesca Storti (Coordinatore)
Python programming language D Maurizio Boscaini (Coordinatore)
II semestre From 3/2/20 To 6/12/20
years Teachings TAF Teacher
CyberPhysical Laboratory D Andrea Calanca (Coordinatore)
C++ Programming Language D Federico Busato (Coordinatore)
LaTeX Language D Enrico Gregorio (Coordinatore)
Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
List of courses with unassigned period
years Teachings TAF Teacher
Corso Europrogettazione D Not yet assigned
The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. D Matteo Cristani

Career prospects


Avvisi degli insegnamenti e del corso di studio

Per la comunità studentesca

Se sei già iscritta/o a un corso di studio, puoi consultare tutti gli avvisi relativi al tuo corso di studi nella tua area riservata MyUnivr.
In questo portale potrai visualizzare informazioni, risorse e servizi utili che riguardano la tua carriera universitaria (libretto online, gestione della carriera Esse3, corsi e-learning, email istituzionale, modulistica di segreteria, procedure amministrative, ecc.).
Entra in MyUnivr con le tue credenziali GIA.

University Language Centre - CLA

Allegati


Area riservata studenti


Tutorato per gli studenti

I docenti dei singoli Corsi di Studio erogano un servizio di tutorato volto a orientare e assistere gli studenti del triennio, in particolare le matricole, per renderli partecipi dell’intero processo formativo, con l’obiettivo di prevenire la dispersione e il ritardo negli studi, oltre che promuovere una proficua partecipazione attiva alla vita universitaria in tutte le sue forme.

TUTORATO PER GLI STUDENTI DELL’AREA DI SCIENZE E INGEGNERIA
Tutorato finalizzato a offrire loro un’attività di orientamento che possa essere di supporto per gli aspetti organizzativi e amministrativi della vita universitaria.
Le tutor attualemente di riferimento sono:
  • Dott.ssa Luana Uda, luana.uda@univr.it
  • Dott.ssa Roberta RIgaglia, roberta.rigaglia@univr.it

Tirocini e stage

Le attività di stage sono finalizzate a far acquisire allo studente una conoscenza diretta in settori di particolare attività per l’inserimento nel mondo del lavoro e per l’acquisizione di abilità specifiche di interesse professionale.
Le attività di stage sono svolte sotto la diretta responsabilità di un singolo docente presso studi professionali, enti della pubblica amministrazione, aziende accreditate dall’Ateneo veronese.
I crediti maturati in seguito ad attività di stage saranno attribuiti secondo quanto disposto nel dettaglio dal “Regolamento d’Ateneo per il riconoscimento dei crediti maturati negli stage universitari” vigente.

Tutte le informazioni in merito agli stage sono reperibili al link https://www.univr.it/it/i-nostri-servizi/stage-e-tirocini.
 

Graduation

List of theses and work experience proposals

theses proposals Research area
Analisi e percezione dei segnali biometrici per l'interazione con robot AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Integrazione del simulatore del robot Nao con Oculus Rift AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
BS or MS theses in automated reasoning Computing Methodologies - ARTIFICIAL INTELLIGENCE
Domain Adaptation Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Domain Adaptation Computing methodologies - Machine learning
Dati geografici Information Systems - INFORMATION SYSTEMS APPLICATIONS
Analisi e percezione dei segnali biometrici per l'interazione con robot Robotics - Robotics
Integrazione del simulatore del robot Nao con Oculus Rift Robotics - Robotics
BS or MS theses in automated reasoning Theory of computation - Logic
BS or MS theses in automated reasoning Theory of computation - Semantics and reasoning
Proposte di tesi/collaborazione/stage in Intelligenza Artificiale Applicata Various topics
Proposte di Tesi/Stage/Progetto nell'ambito delle basi di dati/sistemi informativi Various topics

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.