Intelligence artificielle et données
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Cours, examens, TD, TP et exercices de intelligence artificielle et données. Thèmes couverts : machine learning, apprentissage automatique, big data, data science, fouille de données.
This document discusses data reading, the application of regular expressions in R, and how to extract domains from email addresses. It provides coding examples and explanations of the syntax used in regular expressions. The focus is on overcoming challenges associated with unique email formats and counting domain frequencies.
The document provides an overview of methods to import, read, and manipulate tabular data formats in R, such as CSV and Excel files. It discusses the use of built-in functions like `read.csv` and libraries like `readxl` for handling external datasets. Methods to access specific rows, columns, and subsets of these data frames are thoroughly explained. Additionally, the document introduces built-in datasets in R and demonstrates their usage with key functions such as `data()` and `help()`.
This document explains ensemble learning techniques (bagging, random forests, and boosting) for classification and regression problems using Python and Scikit-learn. Bagging involves averaging predictions of multiple predictors to reduce variance and works well with strong predictors. Random forests enhance decision trees by introducing randomness in attribute selection, further reducing variance. Boosting sequentially trains weak classifiers, modifying data distributions to obtain strong combined results. The lab exercises include variance analysis on model accuracy, hyperparameter tuning,...
This document focuses on decision trees for regression using Scikit-learn, guiding the user through creating a regressor and its application. A sinusoidal signal with added noise is used as a dataset to train and test the regression model. Key experiments include analyzing the impact of the decision tree's maximum depth and the level of noise in the dataset, along with an exploration of the Diabetes dataset. The report also involves evaluating model performance using mean squared error and implementing a grid search for hyperparameter tuning.
This document provides an in-depth overview of data integration techniques in Business Intelligence (BI) systems. It explores the ETL (Extract, Transform, Load) process, EII (Enterprise Information Integration), and EAI (Enterprise Application Integration), detailing their characteristics, advantages, disadvantages, and use cases. Core methodologies include data extraction (both real-time and batch modes), data transformations, and strategies for resolving data inconsistencies. Additionally, examples of commercial tools, such as Oracle Warehouse Builder and IBM WebSphere, are highlighted fo...
Ce module traite de l'intégration des données dans le contexte de la Business Intelligence. Il aborde les processus ETL, les outils d'intégration et les différences entre ELT et ETL. Les défis liés à l'intégration de données provenant de sources disparates sont également discutés.
This document outlines the implementation of data warehouse schemas for Adventure Works Cycles, enabling reporting and analytical capabilities for key business metrics. The lab covers designing, creating, and implementing both star and snowflake schemas, including relevant fact and dimension tables. A time dimension table is also created and populated to facilitate time-based analysis of business measures. The resultant schema supports normalized tables, hierarchical relationships, and integrates dimensions like geography and time for enhanced querying and reporting.
This document provides a practical introduction to data warehousing, using a fictional company called Adventure Works Cycles as a scenario. Key tasks include exploring various business data sources, running ETL (Extract-Transform-Load) processes using SQL Server Integration Services, and querying the resultant data warehouse for business insights. The methodology emphasizes staging data, cleansing processes, and centralized storage to solve distributed data challenges. Findings highlight the ability to consolidate data for improved reporting and analysis across multiple business domains.
This document describes a series of exercises focused on generating and interpreting outputs using SPSS statistical software. Tasks include creating frequency tables and cross-tabulations for variables such as travel purpose, global satisfaction, and respondent demographics (e.g., gender, profession). Graphs such as histograms and pie charts are created to visualize data, and analytical insights involve calculating age statistics and average scores for key criteria (ticket price, service quality, and flight punctuality). The exercises emphasize cross-tabulation with percentages for deeper i...
The document discusses a decision-support system module with a focus on data warehouse design for the Tunisian Postal Service. The theoretical portion includes comparisons of data warehouse architectures and scenarios where implementing a data warehouse is unsuitable. A case study emphasizes designing a data warehouse for payment tracking via smart payment cards. The proposed data model needs to support comprehensive reporting on financial metrics such as revenue by provider or zone, client segmentation by transaction behaviors, and promotional campaign targeting.
This document outlines an examination for a module on Decision Support Systems at the Université Virtuelle de Tunis. It includes reflective questions and a case study focusing on the implementation of a Data Warehouse for the Tunisian Post, emphasizing the monitoring of payments through various services. The examination assesses students' understanding of specific concepts and their ability to model information systems.
This document explores methodologies for analyzing retail and insurance data using data warehousing techniques. Retail data analysis focuses on sales performance across dimensions such as product, location, temporal trends, and supplier, with proposed star and snowflake schemas for structuring data. Insurance data analysis delves into policy management and claims tracking, including key metrics such as premiums, transactions, profits, and claims payments. Hierarchical data aggregation strategies and dimensional modeling drive efficiency in querying and reporting, with detailed diagrams and...
This document details a set of exercises aimed at using Apache Spark's RDD functionalities for processing large datasets. The tasks include loading datasets, filtering data based on specific criteria, working with partitions, performing transformations using map and reduceByKey, counting word occurrences in multiple files, and combining results through joins. The methodology emphasizes practical implementation of Spark's RDD operations for handling logs, purchase data, and textual analysis while utilizing caching for optimization.
This document analyzes the strategic deployment involving big data and organizational change, specifically in the automotive manufacturing sector. It discusses the implications of strategic change on organizational efficiency. Two primary applications are highlighted, showcasing the interrelation between big data and effective organizational practices.
The course focuses on high-performance and intensive computing, presenting modern hardware architectures and their performance analysis. It delves into parallel computing paradigms, including the design and programming of efficient parallel algorithms, with an emphasis on automatic parallelization of polyhedral programs. Additionally, the syllabus includes architectural studies, task scheduling in homogeneous and heterogeneous environments, and exploration of tools and environments like OpenMP and MPI for parallel programming.
This module addresses the growing importance of high-performance computing (HPC) in research and industry through parallel systems. It provides an overview of hardware architectures, delves into parallel algorithmic and programming methodologies, and emphasizes techniques for analyzing and designing efficient parallel algorithms. Special attention is given to automatic parallelization of polyhedral programs, utilizing tools like OpenMP and MPI. Key topics include task scheduling in homogeneous and heterogeneous environments.
This document introduces the NoSQL database paradigm in the context of distributed systems and Big Data requirements. It explores scalability issues, highlighting horizontal versus vertical scaling, and explains distributed frameworks like MapReduce for processing large data sets. The CAP theorem is discussed in the context of trade-offs between consistency, availability, and partition tolerance. Additionally, types of NoSQL databases such as key-value stores, column-oriented databases, graph databases, and document-based databases are detailed, providing insights into their strengths, weak...
This document details a Data Warehousing exam administered by the Université Virtuelle de Tunis. It covers two exercises: (1) designing a star schema and a snowflake schema to improve sales data collection and analysis for a chain of magazine stores, with specific analytical indicators and SQL queries for sales analysis; (2) creating star and snowflake schemas for an academic institution to understand factors affecting student exam success, including demographic and course-specific performance data. The document emphasizes the importance of dimensional modeling and schema transformations fo...
This document outlines an exam for the Master Professional in Business Intelligence at the Université Virtuelle de Tunis. It consists of exercises focusing on data modeling and integration for a chain of stores specializing in newspapers and magazines. The exam assesses the ability to create star and snowflake schemas, along with SQL query formulation and analysis of sales data.
This document presents exercises for a Master's level course on data warehousing. It includes tasks on designing data marts to analyze sales data in various contexts, such as disposable dish manufacturing and fast-food restaurants. The exercises also involve creating models in star and snowflake schemas.
This document focuses on the definition, architecture, and methodologies of Data Warehouses (DW). It explains key concepts such as subject-oriented, integrated, non-volatile, and historical data storage principles (Inmon's definition). Additionally, it outlines dimensional modeling techniques like star and snowflake schemas, emphasizing their role in decision-making processes. Architectural approaches by Inmon (Corporate Information Factory) and Kimball (Dimensional Data Warehouse) are compared, highlighting their core differences in data storage and access strategies. Practical steps for i...
This module covers the essentials of Data Warehousing, focusing on the implementation process and modeling concepts. It highlights the importance of data and analysis in decision-making for modern businesses. By the end of the course, participants will be able to explain the objectives of a Data Warehouse and effectively distinguish it from transactional databases.
This document explores the application of fuzzy logic in two main scenarios: quality control in manufacturing and temperature regulation of buildings. The first exercise involves constructing a fuzzy logic-based decision-making system for smartphone quality based on weight and length, utilizing trapezoidal and triangular membership functions. The second exercise designs a temperature regulation system for buildings using fuzzy controllers, with inputs from internal and external temperature sensors, and varying power outputs determined by predefined linguistic terms and membership functions....
The document presents exercises focusing on fuzzy logic inference methods within AI foundations. It progresses from calculating membership degrees of temperature variables to graphical representations of fuzzy functions, explores inference rules via Mamdani's Max-Min method, and concludes with an aggregation of rules using fuzzy control systems defined by linguistic variables. The examples emphasize practical applications like temperature regulation and control systems with multiple inputs and outputs.