Identifying Data 2022/23
Subject (*) Data Warehousing Code 614G01043
Study programme
Grao en Enxeñaría Informática
Descriptors Cycle Period Year Type Credits
Graduate 2nd four-month period
 Third Optional 6
Language
Spanish
Galician
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Computación
Coordinador
Ladra González, Susana
 E-mail
susana.ladra@udc.es
Lecturers
Ladra González, Susana
Silva Coira, Fernando
 E-mail
susana.ladra@udc.es
fernando.silva@udc.es
Web http://moodle.udc.es
General description Como "Almacéns de Datos" ou "Data Warehouse" enténdese todo o relacionado coas base de datos da contorna analítica, ou sexa, as utilizadas no proceso de toma de decisións. Unha parte importante da explotación de datos no ámbito analítico é a aplicación de ferramentas de minería de datos para descubrir coñecemento oculto.

Study programme competencies
Code Study programme competences
A46 Capacidade de integrar solucións de tecnoloxías da información e as comunicacións e procesos empresariais para satisfacer as necesidades de información das organizacións, permitíndolles alcanzar os seus obxectivos de forma efectiva e eficiente, e dándolles así vantaxes competitivas.
B3 Capacidade de análise e síntese
B5 Habilidades de xestión da información
C3 Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida.
C7 Asumir como profesional e cidadán a importancia da aprendizaxe ao longo da vida.
C8 Valorar a importancia que ten a investigación, a innovación e o desenvolvemento tecnolóxico no avance socioeconómico e cultural da sociedade.

Learning aims
Learning outcomes Study programme competences
Coñecer os conceptos de bases de datos necesarios para afrontar o proceso ETL, entender o proceso analítico e diferencialo do operacional, coñecer a arquitectura dun almacén de datos e saber efectuar o deseño e a explotación do mesmo, coa orientación á toma de decisións e incluíndo a utilización de ferramentas de minería de datos. A46
 B3
B5
 C3
C7
C8

Contents
Topic Sub-topic
Introduction to Business Intelligence and Data Warehouse Decision Making
Analytical Environment
Concept of Data Warehouse
Types of Analytical Databases
Data Warehouse Architecture Data Warehouse Components
Data Warehouse Development Orientations
ETL Process
Metadata
Data Warehouse Design Multidimensional Modelling
Conceptual Modelling
Logical Modelling
Advanced Design Concepts
Data Warehouse Exploitation Data Mining
Analytical SQL

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Laboratory practice A46 B3 B5 C3 C7 C8 14 21 35
Problem solving A46 B3 B5 C3 C7 C8 7 14 21
Workbook A46 B3 B5 C7 C8 0 14 14
Mixed objective/subjective test A46 B3 B5 C3 C7 C8 3 0 3
Supervised projects A46 B3 B5 C3 C7 C8 0 14 14
Guest lecture / keynote speech A46 B3 B5 C7 C8 21 42 63
 
Personalized attention 0 0
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Laboratory practice Son clases nas que se desenvolven as competencias procedimentais relacionadas cos contidos da asignatura.
Nelas realizaranse probas e exercicios cuxo obxetivo é madurar os conceptos das clases teóricas, e introduciranse novos conceptos de carácter práctico que acompañaranse de exercicios.
Problem solving Clases nas que se discutirán as estrategias de solución de diversos problemas propostos.
Workbook Se propondrá a lectura de diversos traballos que complementen e axuden a entender os conceptos plantexados.
Mixed objective/subjective test Examen da asignatura que combina conceptos teóricos, prácticos e problemas.
Supervised projects Trabajos realizados baixo a orientación do profesorado, cuxo obxetivos é que os estudintes asuman a responsabilidade do seu propio aprendizaxe e que aprenden "cómo hacer".
Guest lecture / keynote speech Clases teóricas nas que se expoñen os contidos fundamentais da materia, que poden acompañarse da propuesta e a resolución de exemplos.

Personalized attention
Methodologies
Laboratory practice
Problem solving
Description
Both for the ICT practicals and for problem solving, the teaching staff will provide solutions and/or answer the doubts and questions that may arise. A more personalized attention will be developed in the tutorials.

Assessment
Methodologies Competencies Description Qualification
Laboratory practice A46 B3 B5 C3 C7 C8 The maximum mark will be 4 points out of 10 of the subject. The practice must be done individually or in small groups and will be defended orally. 40
Mixed objective/subjective test A46 B3 B5 C3 C7 C8 The maximum mark will be 3.5 points out of 10 for the subject. Several tests will be held during the course, dealing with theoretical concepts and practical assimilation of the subject.

In order to pass the course as a whole, a MINIMUM grade of 0.75 (out of 2) must be obtained in the final (theoretical) mixed test. If this is not the case, the maximum grade for the subject will not be higher than a 4.9 (and therefore the subject will be considered a FAIL). 		35
Supervised projects A46 B3 B5 C3 C7 C8 The maximum mark of the works will be 2.5 points. 25
 
Assessment comments

NOT SHOW:

At the first opportunity, the student who does not do all the mixed tests will have the grade of NOT SHOW.

At the second opportunity any of the parts of the evaluation can be recovered, so that the grades of this opportunity always substitute those of the first one. The student who does not make up any part of the evaluation will have a grade of NO SHOW.

In order to pass the course, it is mandatory to obtain a minimum grade of 0.75 out of 2 in the theoretical mixed test.

ACADEMIC DISPENSATION:

Students officially enrolled part-time who have been granted an official dispensation from attending classes, as stipulated in the regulations of this University, must contact with the responsible of the course within the first two weeks to establish the conditions for submitting and defending the practical exercises and the supervised project.

ADVANCED OPPORTUNITY:

The evaluation in the advanced opportunity will consist of: mixed test (35% of the qualification), practice (40% of the qualification) and supervised project (25% of the qualification). In order to pass the course, it is mandatory to obtain a minimum grade of 0.75 out of 2 in the theoretical part of the mixed test.

Sources of information
Basic Inmon, W. H. (2002). Building the Data Warehouse, 3nd edition. Wiley
Sharda, R. Delen, D.; Turban, E. (2014). Business Intelligence: A managerial perspective on analytics. Prentice Hall
Williams, G. (2011). Data Mining with Rattle and R. Springer
Tan, P.; Steinbach, M.; Kumar, V. (2006). Introduction to Data Mining . Addison-Wesley
Kimball, R.; Ross, M.; Thornthwaite, W.; Mundy, J.; Becker, B. (2008). The Data Warehouse Lifecycle Toolkit, 2nd edition. John Wiley and Sons
Kimball, R.; Ross, M (2013). The Data Warehouse Toolkit, 3rd edition. Wiley

Complementary Golfarelli, M.; Rizzi, S. (2009). Data Warehouse Design: Modern Principles and Methodologies . McGraw-Hill
García-Molina, H.; Ullman, J.; Widom, J. (2009). Database System. The complete book.. Prentice Hall
Mazón López, N.; Pardillo Vela, J.; Trujillo Mondejar. J. C. (2011). Diseño y explotación de almacenes de datos . Editorial Club Universitario
Elmasri, R.; Navathe, S. (2011). Fundamentals of Database Systems. Addison-Wesley
Inmon, W. H.; Strauss, D.; Neushloss, G. (2008). The Architecture for the Next Generation of Data Warehousing . Morgan Kaufman


Recommendations
Subjects that it is recommended to have taken before
Databases/614G01013

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.