Students will be provided with a sound grounding on theoretical, methodological, and technological fundamentals concerning the management and analysis of data in decision support systems, with a specific reference to data warehousing architectural and design issues. Students will learn key elements of data integration and governance, data quality and cleaning, ExtractionTransformation-Loading processes, conceptual, logical, and physical design of data warehouses, storage architectures and scalable parallel processing, use of data warehouses for business reporting and online analytical processing. Students will also learn key elements on conceptual and notational tools for business modelling. Students will be involved in project activities.
Class, project and outside preparation
The course will introduce the main architectural and design issues related to data management and analisys in decision support systems (data warehousing), comparing them with classical transactional data management systems. Introduction Computer aided decision support systems. OLAP vs OLTP. Data warehousing , data mining, business intelligence. Data warehousing system architecture.
Data exploration, integration, and cleaning. Data wrangling and ETL processes. Data governance, data quality, and data cleaning.
Data models for data warehousing. Multidimensional data model. Dimensions,measures, and hierarchies.
Back-end. Storage alternatives and massively parallel query processing. Storage structures and indexing. Materialized views. Query optimization.
Data warehouse design. Conceptual, logical, and physicial design.
Front-end. OLAP queries and reporting. OLAP SQL extensions.
Business process modelling:
Ricevimento: Appointment by email Office: Valle Puggia – 328
Ricevimento: Appointment by email Office: Valle Puggia – 301
Ricevimento: Appointment by email
BARBARA CATANIA (President)
GIOVANNA GUERRINI (President)
LAURA DI ROCCO
GIANNA REGGIO
ELENA ZUCCA
Written examination, oral examination and project discussion
