Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
The Study plan 2019/2020 will be available by May 2nd. While waiting for it to be published, consult the Study plan for the current academic year at the following link.
Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Data warehouse and integration (2020/2021)
Teaching code
4S009009
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
I semestre dal Oct 1, 2020 al Jan 29, 2021.
Learning outcomes
The goal of the course is to enable students to master the engineering methods and processes that are necessary to manage modern information system, and especially data-intensive systems, to operate on large data collections and to understand the utility and methods of business analysis, obtaining useful knowledge to improve the decision-making process.
As a consequence, the course will expose the students to some of the most advanced methodologies adopted to understand the conceptual and technological problems encountered in the design and implementation of solutions based on analyses for complex systems starting from collections of data that must be integrated, organized and analyzed mainly through automatic tools.
Program
Information System Architectures and Heterogeneous Data Integration: structured and non-structured data:
• Introduction to the architectures of modern information systems
• Basics of Data Integration: model heterogeneity, semantic heterogeneity at the schema level, heterogeneity at the data level
• Dynamic data integration: the use of wrappers, mediators, meta-models, ontologies, etc.
• Lightweight data integration
• The future of data integration in the context of Big Data
• Data quality
Data Warehousing and Analysis:
• Data Warehouse Architecture and querying
• Data Warehouse Conceptual Design
• Data Warehouse Logical Design
• Introduction to exploratory data analysis and its applications
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| Matteo Golfarelli, Stefano Rizzi | Data Warehouse Design: Modern Principles and Methodologies | McGraw-Hill Education - Europe | 2009 | ||
| AnHai Doan, Alon Halevy, and Zachary Ives | Principles of Data Integration (Edizione 1) | Morgan Kaufmann | 2012 | Book freely available at https://research.cs.wisc.edu/dibook/ |
Examination Methods
To pass the exam, the students must show that:
- they have understood the concepts related to the theory of database integration and data warehouses and their design;
- they are able to describe the concepts in a clear and exhaustive way;
- they are able to apply the acquired knowledge to solve application scenarios described by means of questions and exercises.
The exam consists of a written test containing some questions about theory concepts, an exercise about the design of an integrated database or data warehouse. It is possible to integrate the written exam with a practical project, assigned by the professor.
