Differenze

Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.

--- mds:lbi:start [15/09/2024 alle 15:34 (10 mesi fa)] – [Exams] Salvatore Ruggieri
+++ mds:lbi:start [03/02/2025 alle 23:27 (5 mesi fa)] (versione attuale) – [Exam sessions] Anna Monreale
@@ Linea 8: / Linea 8: @@
     * [[http://pages.di.unipi.it/amonreale/]]
     * [[anna.monreale@unipi.it]]
-    * Office hours: Tuesday: 11:00-13:00 online using Teams or at the Department of Computer Science, room 374/E (Please ask an appointment by email).
+    * Office hours: Tuesday: 11:00-13:00 online using Teams or at the Department of Computer Science, room 374/E (Please ask for an appointment by email).
     * Telephone +39-050-2213119
@@ Linea 14: / Linea 14: @@
     * KDD Laboratory, Univesità di Pisa
     * [[cristiano.landi@phd.unipi.it]]
-    * Office hours: Tuesday: 14:00-16:00 online using Teams or at the Department of Computer Science, room 343 (Please ask for an appointment by email).
+    * Office hours: Wednesday: 14:00-16:00 online using Teams or at the Department of Computer Science, room 343 (Please ask for an appointment by email).
+**If you are not asking for office hours, always email both instructors and include [LDS] at the beginning of the subject line.**
-The following is the timetable of the whole Decision Support Systems course. The two modules span differently over the semester. The first module will take most of the lessons in September-October. The second module will take most of the lessons in November-December.
+The following is the timetable for the whole Decision Support Systems course. The two modules span differently over the semester. The first module will take most of the lessons from September to October. The second module will take most of the lessons from November to December.
 ^  Day of Week  ^  Hour  ^  Room  ^
@@ Linea 23: / Linea 25: @@
 |  Wednesday  |  16:00 - 18:00  |  Fib H-Lab  |
 |  Thursday  |  11:00 - 13:00  |  Fib A1  |
-|  Friday  |  11:00 - 13:00  |  Fib L1  |
+|  Friday  |  11:00 - 13:00  |  Fib C1  |
-A [[https://teams.microsoft.com/l/team/19%3AqCllWc8f7UVglFSVL_MhR4ZjaLlWkUjUvJ3ROQdLSOA1%40thread.tacv2/conversations?groupId=14d45f09-9ae8-4f9f-afd1-114348877094&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams channel]] is used to post news, Q&A, and other stuff related to the course. The lectures will be only in presence and will **NOT** be live-streamed, but recordings of the lecture or of the previous years will be made available here for non-attending students.
+A [[https://teams.microsoft.com/l/team/19%3AqCllWc8f7UVglFSVL_MhR4ZjaLlWkUjUvJ3ROQdLSOA1%40thread.tacv2/conversations?groupId=14d45f09-9ae8-4f9f-afd1-114348877094&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams channel]] is used to post news, Q&A, and other stuff related to the course. The lectures will be only in presence and will **NOT** be live-streamed, but recordings of the lecture from this or the previous years will be made available for non-attending students.
 ====== Learning Material ======
@@ Linea 40: / Linea 42: @@
 ===== Software=====
-  * Anaconda with Python 3.7 (Please, avoid Python 3.8)
+  * Anaconda (Please avoid Python 3.12)
   * SQL Server 2019 Developer Edition or next:[[https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-ver16|SQL Server 2019 Management Studio]].
   * Visual Studio Community 2022. Install/include SSDT workload in installation manager of visual studio: instructions here Italian: [[https://learn.microsoft.com/it-it/sql/ssdt/download-sql-server-data-tools-ssdt?view=sql-server-ver15#ssdt-for-visual-studio-2022|Data Tools Visual Studio 2022 IT]] English: [[https://learn.microsoft.com/en-us/sql/ssdt/download-sql-server-data-tools-ssdt?view=sql-server-ver15#ssdt-for-visual-studio-2022|Data Tools Visual Studio 2022 EN]].
@@ Linea 46: / Linea 48: @@
   * [[https://powerbi.microsoft.com/it-it/desktop/| Power BI Desktop]]
-**Note**: preconfigured virtual machines can be found in the [[https://teams.microsoft.com/l/channel/19%3a8a60419ca5ec46dabe98174af70283e1%40thread.tacv2/Module%2520II%2520-%2520Laboratory%2520of%2520Data%2520Science?groupId=6bc87f32-e2c1-46b8-9c9f-928cae8bbe4d&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams channel]] for both AMD64 (Intel/AMD) and ARM (Apple Silicon) architectures.
+**Note**: preconfigured virtual machines can be found in the [[https://teams.microsoft.com/l/team/19%3AqCllWc8f7UVglFSVL_MhR4ZjaLlWkUjUvJ3ROQdLSOA1%40thread.tacv2/conversations?groupId=14d45f09-9ae8-4f9f-afd1-114348877094&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams channel]] for both AMD64 (Intel/AMD) and ARM (Apple Silicon) architectures.
 ===== F.A.Q. =====
@@ Linea 52: / Linea 54: @@
   * [[http://www.sid.unipi.it/polo2/studenti/ | F.A.Q.s about the labs]]
   * [[https://start.unipi.it/help-ict/vpn/ | Unipi VPN ]]
-  * [[https://autenticazione.unipi.it/auth/auth.signin | Unipi Authentication]] to access the VPN, make sure that network access services are enabled on you profile. Follow this link to access your Unipi profile.
+  * [[https://autenticazione.unipi.it/auth/auth.signin | Unipi Authentication]] to access the VPN, make sure that network access services are enabled on your profile. Follow this link to access your Unipi profile.
-====== Class calendar - (2024-2026) ======
+====== Class calendar - (2024-2025) ======
-^ ^ Day  ^ Topic ^ Slides  ^ Data/Software ^ References ^ Video Lectures
+^ Day  ^ Topic ^ Slides  ^ Data/Software ^ Video Lectures ^
-|1. |18.09  16:00-18:00| Introduction to the Course. BI Architecture. File data access. |  |-** BI technology:** [[https://cacm.acm.org/magazines/2011/8/114953-an-overview-of-business-intelligence-technology/fulltext | An Overview of Business Intelligence Technology]] - **File access:** {{ :mds:lbi:filesystem.pdf | File System Interface}}  |
+|18.09| Introduction to the Course. BI Architectures. File data access. | {{ :mds:lbi:2024-lds.01.introduction.pdf | Introduction}} {{ :mds:lbi:2024-lds.02.bi_architectures.pdf | BI architectures}} {{ :mds:lbi:2024-lds.03.file_data_access.pdf | Files}} |  | [[https://tinyurl.com/4shae4c4|video]] |
+|25.09| Python Recap. + Exercises | {{ :mds:lbi:2024-lds.04.python.pdf | Python Recap}} | {{ :mds:lbi:2024-lds.04_supplementary.code.zip | supplementary code}} | [[https://tinyurl.com/337t8hxf|video]] |
+|02.10| Python File Access | {{ :mds:lbi:2024-lds.05.fileaccess-python.pdf | Python File Access}} | {{ :mds:lbi:data1.zip | Data ex files}} {{ :mds:lbi:lds04_solutions.zip | LDS04 sol}} | |
+|09.10| RDBMS access protocols | {{ :mds:lbi:2024-lds.06.relational_data_access.pdf | Relational Data Access}} {{ :mds:lbi:ex-customers.pdf}} | {{ :mds:lbi:data-customers.zip | Data ex files 2}} {{ :mds:lbi:lds05_fileaccesspython_solutions.zip |LDS05 sol}}| |
+|23.10| Python DB Access | slides previous lecture | | [[https://tinyurl.com/3dun69db|video]] |
+|24.10| Python DB Access | slides previous lecture |{{ :mds:lbi:2024-sample-db.zip |}} | [[https://tinyurl.com/yyku3xy6|video1]] [[https://tinyurl.com/55nzrh6w|video2]] |
+|29.10| Python DB Access: exercises | slides previous lecture | {{ :mds:lbi:lds05_fileaccesspython_excustomer.zip |ex-customer_sol}} | [[https://tinyurl.com/mtmt4hum|video]] |
+|30.10| SQL Server + SSIS + ToCSV| {{ :mds:lbi:2024-lds.07.sqlserver.pdf |SQL Server}} {{ :mds:lbi:2024-lds.08.etlandssis.pdf | ETL and SSIS}} | | [[https://tinyurl.com/3um46n3t|video]] |
+|06.11| SSIS: FromCSV + Project Discussion | slides previous lecture | | [[https://unipiit.sharepoint.com/:v:/s/a__td_65012/ER6z07WVERNLs1AgQg9qOggBIYYU0YCVZNhL724BiIVKyg?e=FMldZI|Video: Due to connection issues the recording could have some issues  ]] |
+|07.11| SSIS: Pipeline| slides previous lecture | | [[https://unipiit.sharepoint.com/:v:/s/a__td_65012/EVZKAvV9s9xMmz-9DDU-mKIBE0YEkQ5U9bknurxkg-ZY8A?e=oU5eCz|Video: Due to connection issues the recording could have some issues]]|
+|13.11| SSIS: Stratified Sampling| slides previous lecture | {{ :mds:lbi:examples-2024.zip | Practice SSIS}}| [[https://unipiit.sharepoint.com/:v:/s/a__td_65012/EQBoMwBYK1JJvsIPYL2Ksj8Bwemnl2HCSbW8pSeg_vuEcA?e=LMw9JM|Video: Due to connection issues the recording could have some issues ]]|
+|14.11| SSIS: Surrogate Keys |slides previous lecture | |[[https://unipiit.sharepoint.com/:v:/s/a__td_65012/Ec3Y7VKIz2BCgLh3D5ZROmwBjxYxUrmMSFaCK-vtY0_P3Q?e=gaQyv7|Due to connection issues the recording could have some issues]] |
+|19.11| SSIS: Practice | | | |
+|20.11| SSIS: Practice | {{ :mds:lbi:ssis_dissimilarity_idx.pdf |Dissimilarity index}} | | |
+|21.11| CDC Process + Dissimilarity Index | {{ :mds:lbi:lds-ssis-projects-full.zip |}}| | |
+|26.11| Introduction to SSAS + DW|{{ :mds:lbi:2024-lds.09.dwandolap.pdf |DW and OLAP}} | | |
+|27.11| Olab Cube|  {{ :mds:lbi:2024-lds.09.ssas.pdf | SSAS }} |{{ :mds:lbi:foodmart_monreale_full-2024.zip |}} Instructions for the SSAS project: to avoid conflicts in deployment/process follow this steps once the solution is opened: (1) rename the project as <your account>_foodmart (2) from project properties select 'Deployment', then rename the database as <your account>_foodmart; (3) click on the button “show all files” just above “Solution explorer” right click on “view code” on the .database file that is visualized, and then change the ID from current name into <your account>_foodmart, and finally save the file; (4) change the credentials of connection to database on SQL Server. As an alternative solution you mayimport the project from the SSAS server and rename it as <your account>_foodmart (step 4 is still necessary).| |
+|28.11|Metrics + Excel power pivot integration |  | {{ :mds:lbi:foodmart_monreale_complete-2024.zip |}}| |
+|03.12| MDX Queries| Same slides as in the previous lectures | {{ :mds:lbi:query-samples.zip |}}| |
+|04.12| MDX Queries| Same slides as in the previous lectures |  | |
+|05.12| MDX Queries| Same slides as in the previous lectures | {{ :mds:lbi:2024-12-05-queries-demomdx.zip | new version of the query samples}} | |
+|11.12| MDX Queries| | | |
+|12.12| PowerBI + MDX practice| {{ :mds:lbi:2024-12-12-queries-demomdx.zip | File updated with new queries}}| {{ :mds:lbi:mdxqueryies-to_besolved.txt.zip |Queries to be solved}}| |
+|13.12| **No lesson in this date.** | | | |
+|18.12| Room C 1400-16:00| | | |
 ====== 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. **The project of Module II can be discussed only after passing Module I and not later than one year since then.**
+__//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). See [[mds:dsd:start|Module I: Decision Support Databases]] for the theory part. **The project of Module II can be discussed only after passing Module I and not later than one year since then.**
 **PROJECT **
-A project consists in a set of assignements corresponding to a BI process: data integration, construction of an OLAP cube, qurying of a OPLAP cube and reporting.
+A project consists of a set of assignments corresponding to a BI process: data integration, construction of an OLAP cube, querying of the OPLAP cube, and reporting.
-The project has to be performed by a team of 2 students (at most 3 after asking authorization for that to the teachers).
+The project has to be performed by a team of 3 students.
-Each part of the project **must be documented** with a brief pdf report (no more that 2/3 pages) describing your solution.
+Each part of the project **must be documented** with a brief pdf report (no more than 5 pages) describing your solution.
-**Project to be delivered within XXXXXXXX **
+**Final Project version to be delivered within 27/12/2024 **
-  * **Dataset:**
+  * **Dataset:** [[https://drive.google.com/file/d/1FzGqHC0urBhbXUhYQrjbLEhR9CqovOm8/view?usp=sharing|Link]]
+  * First part of the project consists in the assignments described here: {{ :mds:lbi:2024_-_lds_project_-_part_1_20241126.pdf | Project Part 1 (updated 26/11/2024)}}. **Deadline**: 05/12/2024
+  * Second part of the project consists in the assignments described here: {{ :mds:lbi:2024_-_lds_project_-_part_2_20241218.pdf |  Project Part 2 (updated 18/12/2024)}}. **Deadline**: 05/01/2025 (extended)
 **Project to be delivered during the exam sessions **
-Students who did not deliver the above project within XXXXXXX need to ask by email a new project to the teachers. The project that will be assigned will require about 2 weeks of work and after the delivery it will be discussed during the oral exam. For those students, the oral exams will also cover some practical parts that could not be included in the project. ** Please write to all teachers!**
+Students who did not deliver the above project by 05/01/2025 need to ask by email a new project to the teachers. The project that will be assigned will require about 2 weeks of work. After the delivery, the project will be discussed during the oral exam. For those students, the oral exams will also cover some practical parts that could not be included in the project. ** Please write to all teachers!**
@@ Linea 83: / Linea 111: @@
 **Important:** the date of the discussion of the lab project will be communicated to you. The dates at the
 [[https://esami.unipi.it/esami2/|registration website]] regard **only** the written part of the DSD module.
+**Oral Exam Date for LDS during the Winter Sessions:**
+  * 27 Jan
+  * 29 Jan
+  * 30 Jan
+  * 31 Jan
+  * 03 Feb
+  * 04 Feb
+  * 14 Feb
+  * 20 Feb
+  * 21 Feb
 =====Past Editions =====