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teaching:infoh419 [2021/10/17 13:34] ezimanyi [Group Project] |
teaching:infoh419 [2022/09/19 15:10] ezimanyi [Grading] |
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| The course is given during the first semester | The course is given during the first semester | ||
| - | * Lectures on Tuesdays from 2 pm to 4 pm at the room S.UA4.218 | + | * Lectures on Mondays from 10 am to 12 pm at the room S.K.3.401 |
| - | * Exercises on Fridays from 2 pm to 4 pm at the room S.UB4.130 | + | * Exercises on Tuesdays from 2 pm to 4 pm at the room S.P4.1.17 |
| ===== Grading ===== | ===== Grading ===== | ||
| * Group project (30%) | * Group project (30%) | ||
| * Written exam (70%) | * Written exam (70%) | ||
| - | * the exam is open book; notes and books can be used. Laptops and other electronic devices are not allowed. | + | * the exam is open book; notes and books can be used. Laptops and other electronic devices are **not** allowed. Please prepare your paper material in advance. |
| ===== Course Summary ===== | ===== Course Summary ===== | ||
| Relational and object-oriented databases are mainly suited for operational settings in which there are many small transactions querying and writing to the database. Consistency of the database (in the presence of potentially conflicting transactions) is of utmost importance. Much different is the situation in analytical processing where historical data is analyzed and aggregated in many different ways. Such queries differ significantly from the typical transactional queries in the relational model: | Relational and object-oriented databases are mainly suited for operational settings in which there are many small transactions querying and writing to the database. Consistency of the database (in the presence of potentially conflicting transactions) is of utmost importance. Much different is the situation in analytical processing where historical data is analyzed and aggregated in many different ways. Such queries differ significantly from the typical transactional queries in the relational model: | ||
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| ===== Books ===== | ===== Books ===== | ||
| - | * [[https://www.springer.com/9783642546549|Data Warehouse Systems: Design and Implementation]] by Alejandro A. Vaisman and Esteban Zimányi. Springer, 2014. | + | * [[https://link.springer.com/978-3-662-65167-4|Data Warehouse Systems: Design and Implementation]], second edition, Alejandro A. Vaisman and Esteban Zimányi. Springer, 2022. |
| * [[http://www.morganclaypool.com/doi/abs/10.2200/s00299ed1v01y201009dtm009|Multidimensional Databases and Data Warehousing]] by Cristian S. Jensen, Torben Bach Pedersen, and Christian Thomsen. Morgan & Claypool Publishers. | * [[http://www.morganclaypool.com/doi/abs/10.2200/s00299ed1v01y201009dtm009|Multidimensional Databases and Data Warehousing]] by Cristian S. Jensen, Torben Bach Pedersen, and Christian Thomsen. Morgan & Claypool Publishers. | ||
| * [[http://www.mcgraw-hill.co.uk/html/0071610391.html|Data Warehouse Design: Modern Principles and Methodologies]] by Matteo Golfarelli and Stefano Rizzi. McGraw-Hill, 2009 | * [[http://www.mcgraw-hill.co.uk/html/0071610391.html|Data Warehouse Design: Modern Principles and Methodologies]] by Matteo Golfarelli and Stefano Rizzi. McGraw-Hill, 2009 | ||
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| The project of the course consist of 2 parts: | The project of the course consist of 2 parts: | ||
| - | * Part I: Implement the TPC-DS benchmark (deadline 1/11/2021) | + | * Part I: Implement the TPC-DS benchmark (deadline 1/11/2022) |
| - | * Part II: Implement the TPC-DI benchmark (deadline 24/12/2021) | + | * Part II: Implement the TPC-DI benchmark (deadline 24/12/2022) |
| You have free choice to use the tools on which the two benchmarks will be implemented. For example, the TPC-DS benchmark could be implemented on SQL Server Analysis Services, Pentaho Analysis Services (aka Mondrian), etc. Similarly, the TPC-DI benchmark could be implemented on SQL Server Integration Services, Pentaho Data Integration, Talend Data Studio, SQL scripts, etc., which then load the data warehouse on a DBMS such as SQL Server, Oracle, PostgreSQL, etc. | You have free choice to use the tools on which the two benchmarks will be implemented. For example, the TPC-DS benchmark could be implemented on SQL Server Analysis Services, Pentaho Analysis Services (aka Mondrian), etc. Similarly, the TPC-DI benchmark could be implemented on SQL Server Integration Services, Pentaho Data Integration, Talend Data Studio, SQL scripts, etc., which then load the data warehouse on a DBMS such as SQL Server, Oracle, PostgreSQL, etc. | ||
| - | Furthermore, both benchmarks must be implemented with several scale factors, which determine the size of the resulting data warehouse. You DO NOT need to use the scale factors mentioned in the TPC requirements. The pedagogical objectives aimed is that you learn how to properly perform a benchmark. Therefore, you need to estimate the biggest scale factor that you can put on your own computer: this will be your reference scale factor (SF), say SF 1.0, and then you will need to have 3 smaller scale factors, e.g., at 0.1, 0.2, and 0.5 of the full size in order to see the evolution of the performance. | + | Furthermore, both benchmarks must be implemented with several scale factors, which determine the size of the resulting data warehouse. You DO NOT need to use the scale factors mentioned in the TPC requirements. The pedagogical objectives aimed at is that you learn how to properly perform a benchmark. Therefore, you need to estimate the biggest scale factor that you can put on your own computer: this will be your reference scale factor, say 1.0, and then you will need to have 3 smaller scale factors, e.g., at 0.1, 0.2, and 0.5 of the full size in order to see the evolution of the performance. |
| - | The project is carried out in groups of 3-4 persons, which will be the same for the two parts. Before you can submit part I of the project, you will have to register in a group. For this, please send an email to the lecturer with the information about your group by 1/10/2020 at the latest. The submission deadlines for parts I and II are strict. | + | The project is carried out in groups of 3-4 persons, which will be the same for the two parts. Before you can submit part I of the project, you will have to register in a group. For this, please send an email to the lecturer with the information about your group by 1/10/2022 at the latest. The submission deadlines for parts I and II are strict. |
| The deliverables expected for each part of the project are the following: | The deliverables expected for each part of the project are the following: | ||
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| The project evaluation will count for 30% of your total grade. This may seem undervalued, however, putting effort in the project will definitely help you in achieving a better understanding of the course material which will result in a better score in the paper exam which amounts for 70% of the grade. | The project evaluation will count for 30% of your total grade. This may seem undervalued, however, putting effort in the project will definitely help you in achieving a better understanding of the course material which will result in a better score in the paper exam which amounts for 70% of the grade. | ||
| + | |||
| + | ===== Tools of the previous year ===== | ||
| + | |||
| + | SQL Server, PostgreSQL, mySQL, Oracle, SQLite, mariadb, Spark SQL, DB2/Airflow, Microsoft Azure SQL, Citus, AWS Aurora, Google BigQuery, Impala | ||
| ===== Groups of the current year ===== | ===== Groups of the current year ===== | ||
| - | * SQL Server: Nicole Zafalón, Diogo Rapas, Andrés Espinal, Adam Broniewski | + | TBD |
| - | * PostgreSQL: Niccolò Morabito, CHUN HAN LI, Víctor Diví, Filip Sotiroski | + | |
| - | * mySQL: Valada kylynnyk, Yanjian Zhang, Zhicheng Lou, Kainaat Amjid | + | |
| - | * Oracle: El Achouchi Iliass, Belgada Wassim, Ajouaou Soufiane | + | |
| - | * SQLite: Laamiri Achraf, Mareghni Nidhal, Kuete Kamta Frank Jordan | + | |
| - | * mariadb: Tejaswini Dhupad, Himanshu Choudhary, Kamdem Tagne Thomas Borel, Sergio Postigo | + | |
| - | * Spark SQL: Yi Wu, Hang Yu, Zhiyang Guo, Mohammad Zain Abbas | + | |
| - | * DB2/Airflow: Md Jamiur Rahman Rifat, Khushnur Binte Jahangir, Asha Said Seif, Pietro Ferrazzi | + | |
| - | * Microsoft Azure SQL: Davide Rendina, Marita Hernandez, Luiz Fonseca, Zyrako Musaj | + | |
| - | * ScylaDB: Nazgul K. Rakhimzhanova, Mohammad Ismail Tirmizi, Maël Touret, Wassim Kezai | + | |
| - | * AWS Aurora: Hind Bakkali, Gaëlle Frauenkron, Mahmut Asım Onat, Salma Salmani | + | |
| - | * Google BigQuery: Soufian El Bakkali Tamara, Maciej Piekarski, David Silberwasser, Sami Abdul Sater | + | |
| - | * Impala: Yahya Bakkali, Amirmohammad Fallahi, Maxime Hauwaert, Alexandre Libert | + | |
| ===== Examinations from Previous Years ===== | ===== Examinations from Previous Years ===== | ||