Massive Data Processing

CPE Lyon

Reference Material

Big data technologies and methodologies: distributed computing frameworks, data mining algorithms, neural networks, linked open data, and ethical considerations in large-scale data analysis using Hadoop, Hive, and Spark.
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Lectures

Introduction

Introduction (Big Data)

Foundation of big data concepts, challenges, and opportunities in massive-scale data processing and analysis.

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Data Fundamentals

Data Representation & Analysis

Methods for representing, manipulating, processing, and analyzing massive datasets using modern techniques.

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Data Mining

Patterns & Mining Algorithms

Pattern recognition, data mining tasks, and algorithms for extracting insights from large-scale datasets.

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Neural Networks & Ethics

AI Networks & Data Ethics

Artificial neural networks architecture, training methods, and ethical considerations in data usage and AI applications.

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Semantic Web

Linked Open Data

Principles of linked data, RDF, SPARQL, and integration of distributed semantic data sources.

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Distributed Systems

Hadoop, Hive & Spark

Distributed computing frameworks for massive data analysis: Hadoop ecosystem, Hive queries, and Spark processing.

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Practical Work

GitHub Repository

TDM Repository

Hands-on practical exercises covering big data processing, distributed computing, data mining, neural networks, and semantic web technologies. Includes exercises for Hadoop, Hive, Spark, and machine learning implementations.

Access on GitHub