Analytical Information Systems

Code:

MCI0028

Acronym:

SIA

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2016/2017 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Information Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MCI	7	Plano de estudos oficial	2	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

Make the students able to specify a data warehouse and to interpret the results obtained from the associated visualization and analysis models, including the setup of BSC systems.

Learning outcomes and competences

1. Identify opportunities of using analytical information systems.


2. Prepare feasibility studies for analytical IS projects.


3. Design the dimensional model.


4. Specify the analytical models.


5. Specify the metadata for a data warehouse project.


6. Lead a BSC project.


7. Select the techniques to use in analytical IS projects.


8. Interpret the results of such projects.

Working method

Presencial

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

Relational databases, normalization, SQL.

Program

Transactional and analytical systems.

Data quality.

Phases of a data warehousing project.

Process mapping and information needs.

Dimensional modelling. Special cases.

Data extraction, transport and load.

The data Warehouse in production.

Metadata in a data warehouse.

Getting indicators.

Balanced Score Cards systems.

Explicit and implicit data.

Data mining systems.

Identifying rules and metrics.

Classification problems.

Association rules.

Temporal series.

Main algorithms.

Presenting results.

Mandatory literature

Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker; The Data Warehouse Lifecycle Toolkit, 2nd ed., John Wiley & Sons, 2008. ISBN: 978-0470149775

Complementary Bibliography

Ralph Kimball, Margy Ross; The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd ed., John Wiley & Sons, 2013. ISBN: 978-1118530801
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 classes will mix componentes of concepts presentation and techniques demonstration with small exercises and the step-by-step development of a médium-size lab assignment.

To develop integration competences of the multiple facets of analytical IS, the applied project will be based on a study case. This project is intended to achieve an integrated vision of the subjects studied in the classes. This way, the students are expected to raise their sensibility to the problems of data warehousing and their analysis while supplying them with experiences of case studies close to reality.

Software

Oracle

keywords

Physical sciences > Computer science > Database management

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	40,00
Participação presencial	20,00
Trabalho laboratorial	40,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	66,00
Frequência das aulas	56,00
Trabalho laboratorial	40,00
Total:	162,00

Eligibility for exams

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

Calculation formula of final grade

Exercises: classification of the exercises presented in classes.

Project: classification of the group assignment.

Exam: final exam classification.

Final = 20% * Exercises + 40% * Project + 40% Exam
 
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. Class assignments can be handed within a week.

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.