|Responsible unit:||Department of Informatics Engineering|
|Course/CS Responsible:||Master in Informatics and Computing Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|MIEIC||4||Syllabus since 2009/2010||5||-||6||56||162|
The students should be able to design, build and explore data warehouses.
(1) Plan and manage the lifecycle of a data warehouse project; (2) Identify the requirements and the data sources; (3) Design a suitable dimensional model; (4) Design and implement a data extraction, transformation and loading process ; (5) Specify and implement applications and models to access and visualize the data warehouse information; (6) Optimize the data warehouse through the creation of aggregations and indexes; (7) Build and explore multidimensional implementations; (8) Define the required metadata for audit purposes.
Relational databases, normalization, SQL.
Data warehouses: concept, architecture and approaches.
The data warehouse project lifecycle. Planning, requirements, source identification.
Project: selecting dimensions; granularity; fact tables.
Specific data models.
Heterogeneity, feeding and data migration strategies.
Data quality: audit and cleanning.
Access to large data sets; developing data marts.
Multidimensional modeling. Aggregation and data visualization.
The lectures follow a mixed expositive and problem solving method, where the discussion of new topics and the description of techniques is intertwined with the resolution of exercises and lab work. Due to the applied goals of the course, the assessment method is 50% based on a medium sized lab assignment, executed by two person groups, preferably with real data, in which the several steps of a data warehouse project are followed. In the end, besides the project report, an oral presentation and discussion takes place, as happens in real world projects. The remaining 50% correspond to an individual written exam, where the students have the opportunity to demonstrate their mastering of the issues not covered by their lab assingment.
|Frequência das aulas||42,00|
Distributed evaluation requires a minimum of 7,5/20.
Final Exam: 50%
Lab assignment: 50%
Final exam must score more than 7,5.
Medium size lab assignment.
Students with special status must deliver and present the lab assignment and answer the examination at the same dates as the other students.
The final exam can be improved with an Exam for Classification Improvement.
The lab assignment can be improved by a new assignment to be defined by the teacher.
Pre-requisites: relational model, SQL, normalization theory.