2024
Integration and Analytical Processing of Information
Name: Integration and Analytical Processing of Information
Code: INF13209L
3 ECTS
Duration: 15 weeks/78 hours
Scientific Area:
Informatics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Sustainable Development Goals
Learning Goals
Upon completing the course unit students will have to demonstrate that they have acquired knowledge, skills and aptitudes in the following aspects:
- In operational and decision support systems;
- In the fundamental aspects for the structuring of a data warehouse, including its architecture and design methods;
- Forms of loading, cleaning, and data integration in the data warehouse;
- About NoSQL data models;
- Relevant applications in NoSQL;
- About methods and data analysis in data warehousing and NoSQL;
- On methodologies associated to Business Intelligence;
- In work group work and in written and oral communication.
- In operational and decision support systems;
- In the fundamental aspects for the structuring of a data warehouse, including its architecture and design methods;
- Forms of loading, cleaning, and data integration in the data warehouse;
- About NoSQL data models;
- Relevant applications in NoSQL;
- About methods and data analysis in data warehousing and NoSQL;
- On methodologies associated to Business Intelligence;
- In work group work and in written and oral communication.
Contents
1. Transactional (OLTP) and analytical (OLAP) systems
2. OLAP systems architecture, design and construction
3. Data Warehousing
3.1. Architecture
3.2. Design. Bus matrix.
3.3. Dimensional Model
3.4. Data Warehouse Extraction, Transformation, Cleansing, and Loading
3.5. Analysis of various applications
3.6. Data aggregation
4. NoSQL Databases
4.1. Differences between NoSQL and Relational Databases
4.2. Main NoSQL models: key-value, column family, document and graph.
4.3. Relevant applications in each of the NoSQL model types
4.4. NoSQL technologies
5. Analysis Models in Business Intelligence
5.1. BI Lifecycle and Implementation
5.2. BI User Models
5.2.1. Multidimensional Models
5.2.2. Cubes
5.2.3. Mixed methodologies
2. OLAP systems architecture, design and construction
3. Data Warehousing
3.1. Architecture
3.2. Design. Bus matrix.
3.3. Dimensional Model
3.4. Data Warehouse Extraction, Transformation, Cleansing, and Loading
3.5. Analysis of various applications
3.6. Data aggregation
4. NoSQL Databases
4.1. Differences between NoSQL and Relational Databases
4.2. Main NoSQL models: key-value, column family, document and graph.
4.3. Relevant applications in each of the NoSQL model types
4.4. NoSQL technologies
5. Analysis Models in Business Intelligence
5.1. BI Lifecycle and Implementation
5.2. BI User Models
5.2.1. Multidimensional Models
5.2.2. Cubes
5.2.3. Mixed methodologies
Teaching Methods
The theoretical-practical sessions will comprise (1) lecture/discussion of certain concepts, (2) instructor demonstrations of these same concepts, and other features, and (3) student computer lab sessions working with these same concepts and features. The purpose of this pedagogical approach is to introduce and reinforce ideas and skill sets so that students can master these on their own after hours. To bring this knowledge to a highly proficient, professional level, students will have to spend time and effort outside of hours.
Assessment
Method A: Project with three components - 50%; Two tests during the semester - 40% (20% + 20%); Class participation (10%)
Method B: Project with three components - 50%; Exam - 40%; Class participation (10%)
Students with special statutes may substitute participation for biweekly questionnaires.
Method B: Project with three components - 50%; Exam - 40%; Class participation (10%)
Students with special statutes may substitute participation for biweekly questionnaires.
Teaching Staff (2023/2024 )
- Carlos Alberto Zany Pampulim Martins Caldeira [responsible]
