This course introduces the concepts of database technology used in Business Intelligence. More precisely, this includes multidimensional databases and data warehouses, as well as ETL (Extraction, Transformation and Load) processes and basic concepts of dashboarding. Necessary techniques will be presented for designing, implementing, exploiting, and maintaining data warehouses, paying special attention to spatio-temporal data.
A particular focus will be given on the problems posed by heterogeneous data integration and data quality. The students will learn how to define, measure and maintain data quality in the context of data warehousing. Classical notions of data warehousing and OLAP are developed: ETL, architecture, conceptual and logical design, query processing and optimization. At the end of this course, the student will know how to efficiently design, construct and query a data warehouse to create effective visualizations.
Teachers
Person in charge
Petar Jovanovic (
)
Others
Xavier Oriol Hilari (
)
Weekly hours
Theory
1.9
Problems
0
Laboratory
1.9
Guided learning
0
Autonomous learning
6.85
Competences
Transversal Competences
Teamwork
CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.
Third language
CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.
Entrepreneurship and innovation
CT1 - Know and understand the organization of a company and the sciences that govern its activity; have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit. Being aware of and understanding the mechanisms on which scientific research is based, as well as the mechanisms and instruments for transferring results among socio-economic agents involved in research, development and innovation processes.
Basic
CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
Generic Technical Competences
Generic
CG1 - Identify and apply the most appropriate data management methods and processes to manage the data life cycle, considering both structured and unstructured data
Technical Competences
Especifics
CE2 - Apply the fundamentals of data management and processing to a data science problem
CE3 - Apply data integration methods to solve data science problems in heterogeneous data environments
CE5 - Model, design, and implement complex data systems, including data visualization
CE7 - Identify the limitations imposed by data quality in a data science problem and apply techniques to smooth their impact
Objectives
Be able to model multidimensional data warehouses and visually analyze their data
Related competences:
CT3,
CT5,
CT1,
CE3,
CE5,
CB6,
CB7,
CB8,
CB9,
CB10,
Be able to apply specific physical design techniques for decisional systems
Related competences:
CT3,
CT5,
CG1,
CE2,
CE5,
CB6,
CB7,
CB8,
CB9,
Be able to design and implement data migration processes (i.e., ETL)
Related competences:
CT3,
CT5,
CG1,
CE2,
CE3,
CE5,
CE7,
CB6,
CB7,
CB8,
CB9,
CB10,
Contents
Introduction
Comparison of operational and decisional systems; Metadata
Data warehousing architectures
Corporate Information Factory; DW 2.0
Database physical desing for analytical queries
Star-join and join indexes; Bitmaps; Materialized views; Spatio-temporal data
Extraction, Transformation and Load
Data quality; Schema and Data Integration; ETL management
Visualization and descriptive analytics
Key Performance Indicators; Dashboarding
Activities
ActivityEvaluation act
Theoretical lectures
In these activities, the lecturer will introduce the main theoretical concepts of the subject. Besides lecturing, cooperative learning techniques will be used. These demand the active participation of the students, and consequently will be evaluated. Objectives:132 Contents:
The student will be asked to practice the different concepts introduced in the theoretical lectures. This includes problem solving either on the computer or on paper. Objectives:132 Contents:
Lectures: The teacher presents the topic. Students follow the lesson, take notes, and prepare additional material outside of class. They may also be asked to carry out assessment activities within these sessions.
Laboratory: Some representative tools will be used for the application of theoretical concepts (e.g., PotgreSQL, Oracle, Talend, Tableau). The course includes continuous hands-on through a course project, divided into three logical blocks: data warehouse modelling, data integration and migration (ETL), and descriptive visualisation, in which the students will work in teams. There will be three project deliverables outside the class hours, while in the class the students will be as well individually assessed about the knowledge acquired during each project block.
Evaluation methodology
Final grade = max(20%EP+40%EF ; 60% EF) + 40% P
EP = partial (mid term) exam mark
EF = final exam mark
P = Weighted average of the marks of the project deliverables
Transforming Data With Intelligence (former Data Warehouse Institute) http://tdwi.org
MSCA-ITN-Erasmus Joint Doctorate on Data Engineering for Data Science https://deds.ulb.ac.be
Previous capacities
Basic knowledge on relational databases and SQL.
Specifically, it will be assumed knowledge on:
- UML class diagrams
- Relational algebra
- SQL queries
- Relational views
- B-tree operations (i.e., insertion and splits)
- Basic concepts on physical query optimization