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High Performance and Scalable Analytics Module

Mining with big data or big data mining has become an active research area. Running current analytical methodologies and software tools on a single personal computer cannot efficiently deal with very large datasets. Distributed computing platforms are a scalable solution for big data mining, obtained by dividing a large problem into smaller ones that are concurrently solved by many single processor/machine. This course aims at teaching the basic theoretical concepts behind the MapReduce distributed computing paradigm, and Hadoop in particular, and at building expertise in the practical usage of high performance computing tools for data engineering, analysis and mining. In particular the students will learn how the classical data mining algorithms can be applied on Big Data using Hadoop (Spark & MLlib).

It is part of the Master in Big Data Analytics & Social Mining at the University of Pisa (https://www.masterbigdata.it).

The author did not intend to violate any copyright on figures or content. In case you are the legal owner of any copyrighted content, please contact info@sobigdata.eu and we will immediately remove it

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  • Introduction to Parallel ComputingPDF

    This lecture focuses on providing an introduction to Parallel Computing

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  • Introduction to HadoopPDF

    This lecture focuses on providing an introduction to Hadoop, an open-source...

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  • Hadoop PatternsPDF

    This lecture focuses on Hadoop Patterns

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  • Remote Connection and HDFSPDF

    This lecture focuses on Remote Connection and the Hadoop Distributed File...

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  • Exercises for Remote Connection and HDFS LectureZIP

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  • Introduction to SparkPDF

    This lecture provides an introduction to Spark, which consists of a driver...

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  • Exercises for Introduction to SparkZIP

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  • Introduction to Spark SQLPDF

    This lecture provides an introduction to Spark SQL, Relational Data...

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  • Hadoop Ecosystem and ArchitecturePDF

    This lecture focuses on Hadoop Ecosystem and Architecture

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  • Data Mining with Spark (MLLIB)PDF

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Additional Info
Field Value
Availability On-Site
Course UNIPI Master in Big Data Analytics & Social Mining
Keywords Big Data
Keywords Social Mining
Keywords Distributed
Keywords Real-use Cases
Keywords Parallel Computing
Keywords Hadoop
Keywords Spark
Keywords Spark SQL
Keywords Machine Learning
Keywords Correlation
Keywords Support Vector Machines
Keywords Regression
Keywords Classification
Keywords Clustering
Keywords Word2Vec
Length 418 slides, 4 exercise repositories
Lesson number 9
Prerequisites None
Provider Institution ISTI-CNR
Target users Social Scientists
Target users Data Scientists
Target users Professionals
Target users Other
Thematic Cluster Text and Social Media Mining [TSMM]
Thematic Cluster Social Network Analysis [SNA]
Thematic Cluster Human Mobility Analytics [HMA]
Thematic Cluster Web Analytics [WA]
Thematic Cluster Social Data [SD]
Training material typology Slides
Training material typology Other
system:type TrainingMaterial
Management Info
Field Value
Author BRAGHIERI MARCO
Maintainer BRAGHIERI MARCO
Version 1
Last Updated 29 January 2025, 19:13 (CET)
Created 29 June 2018, 11:34 (CEST)