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====== Decision Support Systems - Module I (6 ECTS): Decision Support Databases A.Y. 2022/23 ======

This is the first module of [[mds:dss:start|Decision Support Systems]] (801AA, 12 ECTS), previously called [[mds:dsd:2021|Decision Support Databases]] (662AA, 6 ECTS). 

The module 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. Specific 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.

<html><!--<p style="color:#FF0000";><b>The server managing video-recordings and SQL Server is DOWN till Monday 23 November.</b></p>--></html>
=====Instructor=====

  * **Salvatore Ruggieri** 
    * Università di Pisa
    * [[http://pages.di.unipi.it/ruggieri/]]
    * [[salvatore.ruggieri@unipi.it]]  
    * **Office hours:** Wednesdays h 14:00 - 16:00 or by appointment, at the Department of Computer Science, room 321/DO, or via Teams.


=====Classes=====

^  Day of Week  ^  Hour  ^  Room  ^ 
|  Wednesday  |  11:00 - 13:00  |  Fib C1  |
|  Thursday  |  14:00 - 16:00  |  Fib M1  |


=====Mandatory teaching material =====

  * **[DW]** A. Albano, S. Ruggieri. [[http://fondamentidibasididati.it/wp-content/uploads/2020/11/DWessential-2021-C3-12-21.pdf|Decision Support Databases Essentials]], University of Pisa, 2 December 2021. 
  * **[DB]** A. Albano. [[http://fondamentidibasididati.it/wp-content/uploads/2020/11/DBEssential-2021-C30-11-21.pdf|DB Essentials]] and [[http://fondamentidibasididati.it/wp-content/uploads/2020/11/DBEssential-2020-Soluzioni-C30-11-21.pdf|solutions to exercises]], University of Pisa, 1 December 2020. This is a self-contained excerpt (in English) from the book [[http://fondamentidibasididati.it|Fondamenti di basi di dati]] (in Italian, free download).
  * Examples of [[http://patterns.di.unipi.it/dsd/DSDsamples.pdf|written exams with solutions]] and [[http://patterns.di.unipi.it/dsd/dsd2020sample.pdf|written exam]].
=====Software=====

  * [[http://fondamentidibasididati.it/index.php/download/|JRS]] for practicing with logical and physical SQL query plans. JRS requires [[https://www.oracle.com/java/technologies/downloads/#java8|Java SE Runtime Environment 8u341]] (need to register to download)
  * [[https://docs.microsoft.com/en-us/sql/azure-data-studio/download|Azure Data Studio]] or [[https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms|SQL Server Management Studio]] client for connecting to SQL Server DBMS Foodmart database
  * [[https://start.unipi.it/en/help-ict/vpn/|Access to University digital services through VPN]] connect to unipi VPN (unless you are already in the unipi.it network) for accessing the Foodmart database

=====Preliminary program and calendar=====

  * [[https://esami.unipi.it/programma.php?c=57058&aa=2022|Preliminary program]].
  * [[https://didattica.di.unipi.it/en/master-programme-in-data-science-and-business-informatics/academic-calendar-2022-2023/|Calendar of lessons]].


=====Exams=====

__//There are no mid-terms//.__ The exam of Decision Support Systems (801AA, 12 ECTS) consists of a written part and an oral part on the topics of the first module (50% of the final grade), and a lab project with discussion on the topics of the second module (50% of the final grade). 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. See [[mds:lbi:start|Module II: Laboratory of Data Science]] for the lab project.

Registration to the written exam is mandatory (**look at the deadline for registering!**): [[https://esami.unipi.it/esami2/|register here]]\\

^  Date  ^  Hour  ^  Room  ^  Notes  ^

<html><! --
|  16/3/2023  |  14:00 - 16:00  |  M1  | [[https://didattica.di.unipi.it/en/appelli-straordinari/|Extra-ordinary exam]] |
-->
</html>

=====Teams channel =====

A [[https://teams.microsoft.com/l/team/19%3aE3in1CcEGzs_W-8NNuQbNkuua_UaPIAzamLvzVNswdw1%40thread.tacv2/conversations?groupId=279299f3-aa07-48b1-8ec6-b2fd1a6d125d&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams channel]] will be used to post news, Q&A, and other stuff related to the course.

=====Class calendar =====

Lessons will be **NOT** be live-streamed, but recordings of past years are available here for non-attending students.\\

Recordings and teaching material are **password protected**. Ask the teacher for credentials.\\

To watch the recordings online, you must be connected to the [[https://start.unipi.it/en/help-ict/vpn/|unipi.it VPN]]. Alternatively, right click on the link and download the whole file, then watch it locally on your device using e.g. [[http://www.videolan.org/vlc/|VLC media player]].

**2022-01.** //Thursday 15 September 2022, 14-16// **[DW: 1.1-1.2]**  [[http://patterns.di.unipi.it/dsd/video/dsd01_20220915.mp4|rec01 audio-video (.mp4) current year]]

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.

**2022-02.** //Wednesday 21 September 2022, 11-13// **[DW: 1.3-1.7]**  [[http://patterns.di.unipi.it/dsd/video/dsd02_20180919.flv|rec02 audio-video (.flv) 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.

**2022-03.** //Thursday 22 September 2022, 14-16// **[DB: 1.1, 2.1-2.5]** [[http://patterns.di.unipi.it/dsd/video/dsd03_20210921.mp4|rec03 audio-video (.mp4) past years]]

Recalls: the Object Data Model. [[http://patterns.di.unipi.it/dsd/dsd.03.assignments.pdf|Exercises at home (Assignments I and II) for the lesson 2022-05]].

**2022-04.** //Wednesday 28 September 2022, 11-13// **[DW: 2.1]**  [[http://patterns.di.unipi.it/dsd/video/dsd04_20170929.flv|rec04 audio-video (.flv) 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.
[[http://patterns.di.unipi.it/dsd/dsd.04.assignments.pdf|Exercises at home (University exams) for the lesson 2022-05]].

**2022-05.** //Thursday 29 September 2022, 14-16// **[DW: 2.1, A.1]**  [[http://patterns.di.unipi.it/dsd/video/dsd05_20210928.mp4|rec05 audio-video (.mp4) past years]]

The example of a data model for Master program exams. Presentation and discussion of the Hospital case study.  [[http://patterns.di.unipi.it/dsd/dsd.05.assignments.pdf|Exercises at home (Assignment III) for the lesson 2022-07]].

**2022-06.** //Wednesday 5 October 2022, 11-13// **[DB: 3.1-3.2]**  [[http://patterns.di.unipi.it/dsd/video/dsd06_20211001.mp4|rec06 audio-video (.mp4) past years]]

Recalls: the relational model and relational algebra. Exercises. 
[[http://patterns.di.unipi.it/dsd/dsd.06.assignments.pdf|Exercises at home (Assignment IV) for the lesson 2022-08]].

**2022-07.** //Thursday 6 October 2022, 14-16// **[DW: 2.1, 2.2, A.1, B.1]** [[http://patterns.di.unipi.it/dsd/video/dsd07_20211005.mp4|rec07 audio-video (.mp4) 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. [[http://patterns.di.unipi.it/dsd/dsd.07.assignments.pdf|Exercises at home (Travel agency) for the lesson 2022-09]].

**2022-08.** //Wednesday 12 October 2022, 11-13//  **[DB: 3.2-3.4]** [[http://patterns.di.unipi.it/dsd/video/dsd08_20211008.mp4|rec08 audio-video (.mp4) past years]]

Recalls: the relational model and relational algebra. Logical trees. [[http://patterns.di.unipi.it/dsd/dsd.08.exercises.pdf|Exercises with JRS]].  [[http://patterns.di.unipi.it/dsd/dsd.08.assignments.pdf|Exercises at home (Airline companies) for the lesson 2022-09]].

**2022-09.** //Thursday 13 October 2022, 14-16// **[DW: A.2, B.2]** [[http://patterns.di.unipi.it/dsd/video/dsd09_20211012.mp4|rec09 audio-video (.mp4) past years]]

Discussion of students' solutions of conceptual and logical design case studies.  

**2022-10.** //Wednesday 19 October 2022, 11-13// **[DW: 3.1-3.5]** [[http://patterns.di.unipi.it/dsd/video/dsd10_20211015.mp4|rec10 audio-video (.mp4) past years]]

Data Warehouse design approaches. Data mart logical design. 

**2022-11.** //Thursday 20  October 2022, 14-16// **[DW: 3.1-3.5]** [[http://patterns.di.unipi.it/dsd/video/dsd11_20221020.mp4|rec11 audio-video (.mp4) current year]]

Slowly changing dimensions, fast changing dimensions, shared dimensions. Recursive hierarchies. Multivalued dimensions. [[http://patterns.di.unipi.it/dsd/dsd.11.assignments.pdf|Exercises at home (Travel agency extended) for the lesson 2022-12]].

**2022-12.** //Wednesday 26 October 2022, 11-13//  **[DW: 4.1-4.8]** [[http://patterns.di.unipi.it/dsd/video/dsd12_20211022.mp4|rec12 audio-video (.mp4) past years]]

A DW to support Analytical CRM Analysis. Wrap up on DW design.  [[http://patterns.di.unipi.it/dsd/dsd.12.assignments.pdf|Exercises at home for the lesson 2021-14]].

**2022-13.** //Thursday 27 October 2022, 14-16//  **[DW: 2.3, 2.4]**[[http://patterns.di.unipi.it/dsd/video/dsd13_20211026.mp4|rec13 audio-video (.mp4) past years]]

Multidimensional Cube model: OLAP Operations. The extended cube and the lattice of cuboids. Pivot tables in Excel. PowerPivot.\\
**Additional learning material:**
  * G. Harvey. Excel 2013 All-in-One For Dummies, 2013. [[http://patterns.di.unipi.it/dsd/PivotTable2013BookVIIchpt2.pdf|Chp. VII-2]] and [[http://patterns.di.unipi.it/dsd/HerbalTeas.xlsx|example data for pivot table]].
  * [[https://support.office.com/en-us/article/power-pivot-overview-and-learning-f9001958-7901-4caa-ad80-028a6d2432ed|Power Pivot overview]].

**2022-14.** //Wednesday 2 November 2022, 11-13//  **[DB: 4.1-4.2,5.1-5.11]** [[http://patterns.di.unipi.it/dsd/video/dsd14_20221102.mp4|rec14 audio-video (.mp4) current year]]

Recalls on: DBMS, from SQL to extended relational algebra. Exercises. 
[[http://patterns.di.unipi.it/dsd/dsd.14.assignments.pdf|Exercises at home for the lesson 2021-15]].

**2022-15.** //Thursday 3 November 2022, 14-16//   **[DW: 5.1-5.3]** [[http://patterns.di.unipi.it/dsd/video/dsd15_20211102.mp4|rec15 audio-video (.mp4) past years]]

OLAP systems. Data Analysis Using SQL. Simple reports. Examples. Moderately Difficult Reports. Solutions in SQL. 
[[http://patterns.di.unipi.it/dsd/dsd.15.foodmart.pdf|Foodmart datawarehouse schema]].

**2022-16.** //Wednesday 9 November 2022, 11-13//  **[DW: 5.4-5.5]** [[http://patterns.di.unipi.it/dsd/video/dsd16_20211105.mp4|rec16 audio-video (.mp4) past years]]

Examples of variance reports. Very Difficult Reports without Analytic SQL. Example of reports with ranks. Analytic Functions with the use of partitions and running totals. Examples.  [[http://patterns.di.unipi.it/dsd/dsd.16.assignments.pdf|Exercises at home for the lesson 2021-17]].

**2022-17.** //Thursday 10 November 2022, 14-16//   **[DW: 5.5-5.6]** [[http://patterns.di.unipi.it/dsd/video/dsd17_20211109.mp4|rec17 audio-video (.mp4) past years]]

Analytic Functions with the use of moving windows. Examples. Exercises on Analytic SQL. [[http://patterns.di.unipi.it/dsd/dsd.17.assignments.pdf|Exercises during the lesson and at home]] and [[http://patterns.di.unipi.it/dsd/dsd.17.solutions.txt|solutions]].

**2022-18.** //Wednesday 16 November 2022, 11-13//  **[DB: 6.1-6.6, 6.8, 7.1-7.2]** [[http://patterns.di.unipi.it/dsd/video/dsd18_20211112.mp4|rec18 audio-video (.mp4) past years]]

Recalls of relational DBMS internals: Storage, Indexing and Query Evaluation. Physical operators and physical plans for projection, selection, joins and grouping. Examples.

**2022-19.** //Thursday 17 November 2022, 14-16// **[DW: 6.1-6.4]** [[http://patterns.di.unipi.it/dsd/video/dsd19_20211116.mp4|rec19 audio-video (.mp4) 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.

**2022-19 bis.** //Wednesday 23 November 2022, 11-13// **[DW: 6.5-6.8]**  [[http://patterns.di.unipi.it/dsd/video/dsd24_20211203.mp4|rec24 audio-video (.mp4) past years]]

**For attending students:** Seminar (in Italian): //Sistema per l’analisi di dati statici di supporto alle decisioni// (V. Minei and R. Mosca, [[https://www.sadasdb.com/en/|Sadas s.r.l.]]

**For non-attending students:** Data Warehousing trends: column-oriented DW, main-memory DW, Big Data framework. (see recorded lesson from past years).


**2022-20.** //Thursday 24 November 2022, 14-16// **[DW: 7.1-7.7]**[[http://patterns.di.unipi.it/dsd/video/dsd20_20211119.mp4|rec20 audio-video (.mp4) 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.  [[http://patterns.di.unipi.it/dsd/dsd.20.assignments.pdf|Exercises at home for the lesson 2022-21]].

**2022-21.** //Wednesday 30 November 2022, 11-13// **[DW: 8.1-8.2, DB: 3.5.1-3.5.4]** [[http://patterns.di.unipi.it/dsd/video/dsd21_20221130.mp4|rec21 audio-video (.mp4) 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.

**2022-22.** //Thursday 1 December 2022, 14-16// **[DW: 8.3-8.6]** [[http://patterns.di.unipi.it/dsd/video/dsd22_20221201.mp4|rec22 audio-video (.mp4) 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.

**2022-23.** //Wednesday 7 December 2022, 11-13// **[DW: 9.1-9.4]** [[http://patterns.di.unipi.it/dsd/video/dsd23_20211130.mp4|rec23 audio-video (.mp4) 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.


=====Previous years=====

  * [[mds:dsd:2021|Decision Support Databases  A.Y. 2021/22]]
  * [[mds:dsd:2020|Decision Support Databases  A.Y. 2020/21]] (special edition)