The course presents the main approaches to the design and implementation of decision support databases, and the characteristics of business intelligence tools and computer based information systems used to produce summary information to facilitate appropriate decision-making processes and make them more quick and objectives. Particular attention will be paid to themes such as conceptual and logical Data Warehouses design, data analysis using analytic SQL, algorithms for selecting materialized views, data warehouse systems technology (indexes, star query optimization, physical design, query rewrite methods to use materialized views). A part of the course will be dedicated to a collection of case studies.
Lessons will be held at: Polo Didattico “L. Fibonacci”, Via F. Buonarroti 4, Pisa.
|Day of Week||Hour||Room||Type|
|Wednesday||14:00 - 16:00||Fib N1||Lectures|
|Friday||16:00 - 18:00||Fib L1||Lectures|
There are no mid-terms. The exam consists of a written part and an oral part. The written part consists of open questions, small exercises, and a Data Warehouse design problem. Each question is assigned a grade, summing up to 30 points. Students are admitted to the oral part if they receive a grade of at least 18 points. Oral consists of critical discussion of the written part and of open questions and problem solving on the topics of the course. For extra sessions see here. Registration to exams is mandatory: register here
Recordings are password protected. Ask the teacher for credentials.
01. Wednesday 18 September 2019, 14-16 [DW: 1.1-1.2] Recording (past years)
Course overview. Need for Strategic Information. Information Systems in Organizations: Operational and Decision support. Data driven Decision support systems and Business Intelligence applications. From data to information for decision making. Types of data synthesis: Reports, Multidimensional data analysis, Exploratory data analysis.
02. Friday 20 September 2019, 16-18 [DW: 1.3-1.7] Recording (past years)
The data warehouse (DW) and DW architectures. What to model in a DW: Facts, measures, dimensions and dimensional hierarchies. Examples of data analysis. Exercises on data analysis in SQL.