Bases de Dados II

Base Knowledge

Students should master the concepts of relational databases (SQL Language, ER Models, and Transactions).

It is recommended the completion of the following course:
– Databases.

Teaching Methodologies

The following teaching methodologies are used in this course:

1 – Expository method: explanatory method where theoretical foundations and concept are presented by the teacher and discussed with the class, followed by demonstrative examples;

2 – Experimental method: active method where the student develops knowledge through problem solving and the development of individual laboratory projects or group dynamics, being the predominant method.

Each class will consist of two moments:

1 – Introductory presentation: At the beginning of the class, the teacher exposes and discusses the new contents under study with the students;

2 – Practical application: After the introductory presentation, students develop work assignments and programming projects, individually and together, for the practical application of new concepts, independently and under the guidance of the teacher.

Learning Results

It is expected that at the end of the curricular unit the student will be able to:

1. Identify the physical aspects of databases and acquire skills for their administration;

2. Identify and apply data exploration and data analysis techniques;

3. Design and develop approaches to data transformation and analysis;

4. Program a database server using the PL/SQL language.

Program

  1. DBMS architecture
  2. PL/SQL (Data Types, Structures, Exceptions, Cursors, Triggers, Procedures, Functions and Views)
  3. Database project development
  4. Data Storage and Physical Parameters
  5. Query optimization and Indexing
  6. Database administration and security concepts
  7. Introduction to Data Analysis (OLAP, ROLAP and MOLAP; Data Warehousing; Multidimensional Databases; ETL Process; Data Preparation; Data Mining Concepts)

Curricular Unit Teachers

Grading Methods

Final evaluation
  • - an individual project with presentation - 50.0%
  • - an individual written test - 50.0%
Periodic Evaluation
  • - two individual practical assignments - 65.0%
  • - a project (individual or in group) with presentation - 35.0%

Internship(s)

NAO

Bibliography

Caldeira, C. (2012). Data warehousing – Conceitos e modelos. Edições Silabo.

Coronel, C.  & Morris, S. (2019) Database Systems: Design, Implementation, and Management (13th ed.)., Cengage Learning.

Gama, J., Lorena, C., Faceli, K., Oliveira, M., & Carvalho, A. (2017). Extracção de conhecimento de dados: Data mining (3th ed.). Edições Sílabo.

Garcia-Molina, H. (2014). Database systems: The complete book. Prentice Hall.

Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3th ed.). Morgan Kaufmann.

Kimball, R., & Ross., M. (2013). The data warehouse toolkit: The complete guide to dimensional modeling. J. Wiley & Sons.

Santos, M., & Ramos, I. (2017). Business intelligence: Da informação ao conhecimento (3th ed.). FCA.

Silberschatz, A., Korth, H. F., & Sudarshan, S. (2019). Database System Concepts (7th ed.). McGraw-Hill. 

Witten, I., & Frank, E. (2016). Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufman.