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.
Online exams: during the COVID-19 restrictions, the written part and the oral part will be online. For the written part, students will connect to Google Meet (room code: 662AA) and will activate both microphone and web-cam. Each sheet will include name, surname, student id, and it will be signed. A picture of the sheets will be delivered to ruggieri [at] di [dot] unipi [dot] it.
Registration to exams is mandatory (deadline is 5 days before the exam!): register here
|24/7/2020||14:00 - 16:00||Online exam|
|7/9/2020||9:00 - 11:00||Online exam|
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.
03. Wednesday 25 September 2019, 14-16 [DB: 1.1, 2.1-2.5] Recording (past years)
Recalls: the Object Data Model.
04. Friday 27 September 2019, 16-18 [DW: 2.1] Recording (past years)
DW modeling. A conceptual multidimensional data model. Representation of Fact, measures, dimensions, attributes and dimensional hierarchies. Key steps in conceptual design from business questions. How to identify fact types and fact granularity and measure types. How to identify dimensions, dimensional attributes and hierarchies. Examples.
Slides: university requirements.
05. Wednesday 2 October 2019, 14-16 [DW: 2.1, A.1] Recording (past years)
The example of a data model for Master program exams. Presentation and discussion of the Hospital case study.
06. Friday 4 October 2019, 16-18 [DB: 3.1-3.2] Recording (past years)
Recalls: the relational model and relational algebra. Exercises.
07. Wednesday 9 October 2019, 14-16 [DW: 2.1,2.2,A.1] Recording (past years)
More about data mart conceptual design, changing dimensions and advanced data model features. From Conceptual design to relational logical design. Star model, snowflake, and constellation. Logical schema of the Hospital case study.
Friday 11 October 2019, 16-18
Lesson canceled to allow students' participation to the Internet Festival. It will be recovered later on.
08. Wednesday 16 October 2019, 14-16 [DB: 3.2-3.3] Recording (past years)
Recalls: the relational model and relational algebra. Logical trees. Exercises.
09. Friday 18 October 2019, 16-18 [DW: 2.3,2.4] Recording (past years)
Multidimensional Cube model: OLAP Operations. The extended cube and the lattice of cuboids. Pivot tables in Excel. PowerPivot.
Additional learning material:
10. Wednesday 23 October 2019, 14-16 [DW: A.2,3.1-3.5], [DWSol: B.2] Recording (past years)
Discussion of students' solutions of conceptual and logical design case studies: The airline companies. A Data Warehouse Design Methodology. Approaches. Design phases. Requirements specifications.
Friday 25 October 2019, 16-18
Lesson canceled due to institutional duties of the teacher. It will be recovered later on.
11. Wednesday 30 October 2019, 14-16 [DW: 3.1-3.5] Recording (past years)
Data mart logical design. Slowly changing dimensions, fast changing dimensions, shared dimensions. Recursive hierarchies. Multivalued dimensions. Multivalued Dimensional Attributes.
12. Monday 4 November 2019, 16-18, (Recover lesson - Room M1) [DB: 3.4], [DW: 4.1-4.8] Recording (past years)
Recalls on: ODM-to-Relational Mapping. A DW to support Analytical CRM Analysis.
13. Wednesday 6 November 2019, 14-16 [DB: 4.1-4.2,5.1-5.11] Recording (past years)
14. Friday 8 November 2019, 16-18 [DW: 5.1-5.4] Recording (past years)
OLAP systems. Data Analysis Using SQL. Simple reports. Examples. Moderately Difficult Reports. Examples of variance reports. Solutions in SQL.
15. Monday 11 November 2019, 16-18, (Recover lesson - Room C1) [DB: 6.1-6.6, 6.8, 7.1-7.2] Recording (past years)
Recalls of relational DBMS internals: Storage, Indexing and Query Evaluation. Physical operators and physical plans for projection, selection, joins and grouping. Examples.
Software: jrs2019.zip (with pre-loaded example) - see here for full system and book.
16. Wednesday 13 November 2019, 14-16 [DW: 5.5-5.6] Recording (past years)
Very Difficult Reports without Analytic SQL. Example of reports with ranks. Analytic Functions with the use of partitions and running totals. Examples. Analytic Functions with the use of moving windows. Examples.
Software: Azure Data Studio.
Friday 15November 2019, 16-18
Lesson canceled due to institutional duties of the teacher. It will be dsdovered later on.
17. Wednesday 20 November 2019, 14-16 [DW: 6.1-6.4] Recording (past years)
Data Warehouse Systems: Special-Purpose Indexes and Star Query Plan. Bitmap indexes. Join indexes. Star queries optimization and query plans. Examples. Table partitioning.
18. Friday 22 November 2019, 16-18 [DW: 7.1-7.7] Recording (past years)
The problem of materialized views selection. The lattice of views and the greedy algorithm HRU for the selection of materialized views. Examples. Other algorithms for the choice of the views to materialize with a workload and dimensional hierarchies.
19. Monday 25 November 2019, 16-18 (Recover lesson - Room C1) [DW: 8.1-8.2, DB: 3.5.1-3.5.4] Recording (current year)
Recalls of functional dependency properties and how they are used to reason about the properties of the result of a query. Properties of the group-by operator.
19 bis. Wednesday 27 November 2019, 14-16 (Room C1)
Seminar (in Italian): Sistema per l’analisi di dati statici di supporto alle decisioni. Speaker: Vincenzo Minei (www.sadasdb.com).
20. Friday 29 November 2019, 16-18 [DW: 8.3-8.6] Recording (current year)
The problem of evaluating the group-by before the join operator. First case: Invariant grouping. Examples. Other cases: double grouping, grouping and counting. Examples with star queries.
21. Wednesday 4 December 2019, 14-16 [DW: 9.1-9.4] Recording (past years)
The problem of query rewrite to use a materialized view. Hypothesis and two approaches: With a compensation on the logical view plan, and with a transformation of logical query plan. Examples.
22. Friday 6 December 2019, 16-18 [DW: 6.5-6.8] Recording (past years)
Data Warehousing trends: column-oriented DW, main-memory DW, Big Data framework.
23. Wednesday 11 December 2019, 14-16
Examples of written exams with solutions. Q. & A.
24. Friday 13 December 2019, 16-18
Examples of written exams with solutions. Q. & A.