Cours - Intelligence artificielle et données
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Cours de intelligence artificielle et données partagés par des étudiants et des enseignants. Thèmes couverts : machine learning, apprentissage automatique, big data, data science, fouille de données.
The document outlines fundamental concepts of artificial intelligence (AI) and machine learning (ML), including definitions, disciplines, and methodologies. It introduces the main categories of AI (cognitive and pragmatic approaches), describes supervised and unsupervised learning techniques, and discusses practical use cases such as decision support systems, natural language processing, and pattern recognition. Emphasis is placed on the importance of extracting knowledge from data through processes like classification, clustering, and association rule learning, with illustrative examples o...
This document highlights the development of a decision support system for the Arab Tunisian Bank (ATB). Agile methodologies, particularly SCRUM, were employed in conjunction with Business Intelligence tools to create an optimized DataWarehouse and Datamart for analyzing client risk and asset classifications. The solution involved designing conceptual models, exploring ROLAP, MOLAP, and HOLAP approaches, and implementing Extract-Transform-Load (ETL) processes. The outcomes included enhanced data management and reporting capabilities for better financial risk assessment and client monitoring.
This report documents the development of a project aimed at generating calculations of mobile data consumption using advanced tools and methodologies. The authors evaluate existing organizational practices in OOREDOO Tunisie, propose solutions based on SCRUM BI methodology, and employ a robust Data Warehouse using tools such as Talend and Oracle Database. Key outcomes include cleaning, transforming, and integrating large data into an optimized structure, culminating in the creation of interactive dashboards for visualizing subscriber data and mobile data consumption trends.
This document details the development of a solution for estimating the prices of used cars in Tunisia. It follows an agile methodology using SCRUM, incorporating machine learning for price prediction. Core tasks include web scraping from various Tunisian car advertisement websites, data cleaning, and the implementation of linear regression and gradient regression models. The project concludes with the centralization of data in a SQL database and the development of a web interface for user interaction.
This document presents a final year project focused on the study, design, and implementation of a business intelligence dashboard system using Big Data technologies. The project uses tools like Apache Hadoop, Spark, Hive, and Arcadia Data to conceptualize and create dashboards from empirical data collected via probes (Astellia and Otarie). Key methodologies like Agile, GIMSI, and Balanced Scorecard were adopted. The result demonstrates a scalable and functional data visualization solution, offering performance indicators and insights tailored to the needs of the host organization, Orange Tu...
This document details the implementation of a Business Intelligence (BI) tool within the Odoo ERP system. Using the SCRUM methodology, it involves the design and development of various data warehouses to address distinct use cases: sales, purchasing, and stock management. The report also covers the integration of PostgreSQL for data storage and manipulation, and Power BI for visualization. Key findings include the establishment of dynamic dashboards enabling efficient data-driven decision-making for the company WAKAHAW.
Partie 2 - Introduction Apache Spark Apache Spark - Pr sentation Apache Spark est une plateforme de traitement sur cluster g n rique. C'est un moteur de traitement libre, assurant un traitement parall le et distribu sur des donn es massives.
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This 2019 document introduces the concept and application of Big Data technologies. It describes key characteristics like the 5Vs (Volume, Variety, Velocity, Veracity, and Value) and methodologies such as distributed processing, MapReduce, and parallel computing. The syllabus covers Hadoop and Spark frameworks, NoSQL databases, and practical exercises for hands-on learning. Real-world use cases, like healthcare, marketing, and sports analytics, illustrate the transformative potential of Big Data.
This document provides an interactive quiz format to introduce key concepts in data mining, covering its predictive and descriptive applications. It addresses business motives behind data mining, such as uncovering hidden trends and enhancing profitability. Core techniques like decision trees, neural networks, and association rule mining are highlighted, alongside challenges such as ensuring privacy and overcoming technical limitations. Practical applications and critical success factors for data mining in various industries are also explored.
This document introduces the MapReduce paradigm, a distributed computing model to process large datasets, especially in scenarios involving tasks such as data aggregation and analysis. Traditional sequential methods are contrasted with the efficiency and parallelization enabled by MapReduce, with emphasis on how large-scale problems can be divided into smaller subtasks. The two core operations, MAP and REDUCE, are described in depth, along with examples in word frequency counting, web analytics, and finding mutual connections in social graphs. The advantage of automating data distribution a...
This document explores the MapReduce paradigm, designed for processing large data sets through distributed computing. It discusses the traditional sales aggregation problem, outlining inefficiencies and presenting MapReduce as a solution. The framework allows for efficient problem-solving by breaking down tasks and parallelizing the computation necessary for big data analysis.
This document provides an in-depth explanation of the MapReduce paradigm used in large-scale distributed computing. It describes the inefficiencies of traditional solutions to analyze large datasets and introduces MapReduce as an effective alternative. The methodology involves dividing tasks into smaller, parallelizable operations: `MAP` to transform data into key-value pairs and `REDUCE` to aggregate results by key. Various examples, including word frequency analysis, website statistics, and common connections in social graphs, illustrate practical applications of the MapReduce model. The...
This document provides an overview of the MapReduce programming model and its efficiency in processing large datasets. It discusses the traditional methods of data processing and introduces the MapReduce paradigm, emphasizing its divide and conquer approach. The document outlines the operations of MAP and REDUCE, detailing the steps involved in the MapReduce process.
Hadoop is an open-source framework designed for distributed data processing across massive datasets, with scalable and fault-tolerant architecture. Core components include HDFS for distributed storage and MapReduce for distributed computing, with additional tools like Hive, Pig, and HBase for advanced analytics and data management. Inspired by Google's publications, Hadoop leverages principles like data redundancy, clustering, and parallel processing to support Big Data challenges. It has been widely adopted across industries, including by organizations such as Facebook, Google, and Amazon.
This document provides a detailed overview of Hadoop, an open-source framework designed for processing large volumes of data. It discusses its architecture, history, and key features, including performance, fault tolerance, and scalability. Additionally, it highlights major companies utilizing Hadoop and its ecosystem.
Hadoop, an open-source framework managed by Apache and written in Java, facilitates distributed processing of massive datasets using clusters of commodity hardware. Key components include HDFS for distributed storage and MapReduce for parallelized data processing, ensuring fault tolerance, scalability, and cost-efficiency. The ecosystem extends functionality through tools such as Pig, Hive, and Flume for data processing, storage, scheduling, and monitoring. Developed by Doug Cutting and inspired by Google technologies, Hadoop powers numerous enterprises like Amazon, Adobe, Facebook, and Twi...
Hadoop is an open-source framework designed for distributed processing of large data sets across clusters. It facilitates the creation of applications that handle massive volumes of data while ensuring performance and fault tolerance. The technology has evolved under the Apache foundation and is widely used in various enterprises.
This document provides an in-depth critique of MapReduce's limitations, emphasizing its inefficiencies in complex, multi-step computations. It introduces Apache Spark as a more advanced, memory-optimized solution with higher performance, scalability, and interactive APIs for diverse programming languages. Key Spark components like RDDs, DataFrames, Datasets, and its ecosystem libraries (e.g., MLlib, GraphX) are detailed alongside their integration capabilities. The document further outlines Spark's architecture, including its execution model and resource management, while contrasting its pe...
This document discusses the limitations of MapReduce for complex tasks and introduces Apache Spark as a more efficient alternative. It covers the features, ecosystem, and architecture of Spark, emphasizing its ability to handle both batch and real-time processing. Spark is designed for speed, utilizing in-memory data storage, and supports various programming languages for ease of application development.
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This document provides an overview of data interrogation languages used in Big Data environments, with a focus on PIG and HIVE. It aims to equip readers with the knowledge necessary to process and analyze large datasets effectively using these tools. The methodologies discussed include structured querying through HIVE and scripting approaches via PIG. The document emphasizes practical applications in data analytics using Hadoop ecosystems.
This document is an educational resource on Big Data, focusing on HDFS and MapReduce frameworks. It explains HDFS architecture, emphasizing data replication and fault tolerance mechanisms in a master/slave setup with NameNode and DataNode roles. Similarly, it explores the MapReduce processing model, detailing the task distribution and fault tolerance strategies. Both methods prioritize efficient, redundant, and reliable data storage and processing distributed across large clusters.