This module covers the fundamental concepts of management database systems at scale as well as the analysis of existing benchmarks in different application scenarios. Topics include data models (relational); query languages (SQL); implementation techniques of database management systems even at large scale; noSQL databases, temporal, patial, Multimedia, and Deductive Databases. The module will also discuss available large scale multimedia datasets and how to query them as well as the state of the art techniques on how to create benchmarks for testing data analytics techniques. Principles on how to detect mistakes, biases, systematic errors, and other unexpected problems will be analyzed.
The learning objectives are:
Knowledge and understanding
Applying knowledge and understanding
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
The learning objectives are:
Knowledge and understanding
Applying knowledge and understanding
The main teaching methods are as follows:
The main teaching methods are as follows:
1) Models and Languages for Database Management (15 hours)
2) Querying and processing big data (10 hours)
3) Analyzing existing benchmarks (15 hours)
1. Introduction to Business Intelligence and Big Data Analytics (6 hours)
2. Data models for data warehouse (12 hours)
3. BI Architecture (12 hours)
4. Data Visualization (10 hours)
1. R. Elmasri and S. Navathe, Fundamentals of Database Systems, 7th Edition, Pearson, 2016.
2. Denny Lee, Tomasz Drabas, Learning Spark SQL, Packt Publishing, 2017
3. Instructor’s notes
4. Research papers (a list will be published on the page course)