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Data Warehouses

Code: EIC0046     Acronym: ADAD

Keywords
Classification Keyword
OFICIAL Information Systems

Instance: 2014/2015 - 1S Ícone do Moodle

Active? Yes
E-learning page: https://moodle.fe.up.pt/
Responsible unit: Department of Informatics Engineering
Course/CS Responsible: Master in Informatics and Computing Engineering

Cycles of Study/Courses

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

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Gabriel de Sousa Torcato David 3,00

Teaching language

Suitable for English-speaking students

Objectives

The students should be able to design, build and explore data warehouses.

Learning outcomes and competences

(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.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Relational databases, normalization, SQL.

Program

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.
Metadata requirements.

Mandatory literature

Kimball, Ralph 070; The Data Warehouse lifecycle toolkit. ISBN: 0-471-25547-5

Complementary Bibliography

Kimball, Ralph; The data warehouse toolkit. ISBN: 0-471-15337-0
Inmon, W. H.; Building the data warehouse. ISBN: 0-471-08130-2
Oracle Corporation; Oracle® Database - Data Warehousing Guide - 11g Release 2 (11.2), Oracle Corporation, 2011 (Document E25554-01)
Oracle Corporation; Oracle® OLAP - User's Guide - 11g Release 2 (11.2), Oracle Corporation, 2010 (Document E17123-03)

Teaching methods and learning activities

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.

Software

Oracle

keywords

Physical sciences > Computer science > Database management

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Exame 50,00
Participação presencial 0,00
Trabalho laboratorial 50,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 80,00
Frequência das aulas 42,00
Trabalho laboratorial 40,00
Total: 162,00

Eligibility for exams

Distributed evaluation requires a minimum of 7,5/20.

Calculation formula of final grade

Final Exam: 50%
Lab assignment: 50%

Final exam must score more than 7,5.

Examinations or Special Assignments

Medium size lab assignment.

Special assessment (TE, DA, ...)

Students with special status must deliver and present the lab assignment and answer the examination at the same dates as the other students.

Classification improvement

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.

Observations

Pre-requisites: relational model, SQL, normalization theory.

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