Databases

LINGI2172  2018-2019  Louvain-la-Neuve

Databases
6.0 credits
30.0 h + 30.0 h
2q

Teacher(s)
Nijssen Siegfried ;
Language
Anglais
Main themes
  • Data Base Management Systems (objectives, requirements, architecture).
  • The Relational data model (formal theory, first-order logic, constraints).
  • Conceptual models (entity-relationship, object role modeling).
  • Logical database design (normal forms & normalization, ER-To-Relational)
  • Physical database design and storage (tables and keys, indexes, file structures).
  • Querying databases (Relational Algebra, Relational Calculus, data structures, query optimization, SQL)
  • ACID properties (Atomicity, Consistency, Isolation, Durability), Concurrency Control, Recovery techniques.
  • Programming database applications (JDBC, Database Cursors, Object-Relational Mapping).
  • Recent or more advanced trends in the database field (object-oriented databases, Big Data, NoSQL, NewSQL)
Aims

Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • INFO1.1-3
  • INFO2.1-4
  • INFO4.1-4
  • INFO5.1-5
  • INFO6.1, INFO6.4

Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • SINF1.M2
  • SINF2.1-4
  • SINF4.1-4
  • SINF5.1-5
  • SINF6.1, SINF6.4

Given the learning outcomes of the "Master [60] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • 1SINF1.M2
  • 1SINF2.1-4
  • 1SINF4.1-4
  • 1SINF5.1-5
  • 1SINF6.1, 1SINF6.4

Students completing this course successfully will be able to :

  • explain the scenarios in which using a database is more convenient than programming with data files;
  • explain the characteristics of the database approach, where they come from and contrast them with current trends in the database field
  • identify and describe the main functions of a database management system;
  • categorize conceptual, logical and physical data models based on the concepts they provide to describe the database structure;
  • understand the main principles and mathematical theory of the relational approach to database management;
  • design databases using a systematic approach, from a conceptual model through a logical level (i.e., a relational schema) into a physical model (i.e., tables and indexes);
  • Use SQL (DDL) to implement a relational database schema  and distinguish from SQL facilities with respect to the logical vs. physical distinction.
  • query relational databases using SQL (DML) and contrast SQL with relational theory .
  • optimize the performance of databases.
  • understand the benefits and drawbacks of NoSQL databases.
  • use relational databases either directly or from a conventional programming language.

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.

Content
  • Introduction to the entity-relationship model,
  • Bases of the relational model: data structures and algebra,
  • Logic-based relational languages to define and manipulate data,
  • Critical study of the SQL language,
  •  Database application programming,
  •  Functions and architecture of database-management systems,
  •  Management of concurrent database accesses and associated techniques of recovery after failures.
Teaching methods

The objectives are organized along three main axes:

  • Understand: both the historical context, and recent challenges and developments in the database field; relational theory, why is has been invented and how it fits in practice; implementation techniques and major algorithms for data organization, query and transaction processing.
  • Design: from conceptual modeling (e.g. Entity-Relationship, UML) down to physical database tuning (e.g. indexes, query plans), through logical database design (e.g. functional dependencies, normal forms, normalization algorithms) and reasoning (relational algebra, views and constraints).
  • Use: installing and configuring database management systems, creating and tuning databases, using query languages in practice (e.g. SQL), connecting to databases (e.g. call interfaces, ORMs), integrating database systems in software designs.

Theory and practice are acquired by students along those three axes as follows:

  • Theory is dispensed in the traditional way, through two-hours auditorium sessions during the second quarter. The theoretical course follows Elmasri & Navathe's textbook [EN10].
  • Practice is acquired by students through projects of 2-3 weeks each.. Some missions are achieved by groups of four students.
    
  • Both theory and pratical missions are dispensed in English.
Evaluation methods

Final grade following a 75% / 25% rule (final written exam / participation and grade obtained to practical missions during the semester). 25% from practical missions applies in september too.

Other information

Background:

  • LSINF1225 : Basic knowledge of database management,
  • LSINF1121 : good abilities in programming.
Bibliography
  • Ramez Elmasri and Shamkant Navathe Fundamentals of Database Systems. Addison-Wesley Publishing Company, USA, 7th edition, 2010.
  •  Chris J. Date. An Introduction to Database Systems. Pearson Addison-Wesley, Boston, MA, 8 edition, 2004.
  • [Hugh Darwen, An Introduction To Relational Database Theory, 3th Edition, Bookboon, 2009
  • Jean-Luc Hainaut, Bases de Données, Concepts, Utilisation et Développement, 2e Edition, Dunod, 2012
  •  T.M. Connolly and C.E. Begg. Database Systems: A Practical Approach to Design, Implementation, and Management. Number v. 1 in International computer science series. Addison-Wesley, 2005.
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Program title
Sigle
Credits
Prerequisites
Aims
Master [120] in data Science: Statistic
6
-

Master [120] in Computer Science and Engineering
6
-

Master [120] in Mathematical Engineering
6
-

Master [120] in Environmental Bioengineering
6
-

Master [120] in Biomedical Engineering
6
-

Master [120] in Data Science Engineering
6
-

Master [120] in data Science: Information technology
6
-

Master [60] in Computer Science
6
-

Master [120] in Computer Science
6
-