ECTS - University of Pisa
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Distributed Data Analysis and Mining
ROBERTO TRASARTI
Academic year2021/22
CourseDATA SCIENCE AND BUSINESS INFORMATICS
Code687AA
Credits6
PeriodSemester 1
LanguageEnglish

ModulesAreaTypeHoursTeacher(s)
DISTRIBUTED DATA ANALYSIS AND MININGINF/01LEZIONI48
ROBERTO TRASARTI unimap
Prerequisiti (conoscenze iniziali)
  • Data Mining I e II
  • Programmazione Python di base
Prerequisites
  • Data Mining I e II
  • Basic of Python programming language 
Programma (contenuti dell'insegnamento)

Il Data Mining sui Big data è oggi un’area di ricerca molto attiva. L'applicazione delle attuali metodologie analitiche e strumenti software su un singolo personal computer non può gestire in modo efficiente dataset di grandi dimensioni. Le piattaforme di calcolo distribuito sono una soluzione scalabile per il big data mining, attraverso la scomposizione del problema in operazioni più piccole che possono essere eseguite parallelamente su singoli processori / macchine. Il corso propone l’insegnamento di concetti base del paradigma di calcolo distribuito tramite MapReduce dal punto di vista teorico e pratico, in particolare ci si focalizzerà su Hadoop per lo sviluppo di competenze nell'uso di strumenti di calcolo ad alte prestazioni per il data engineering, l’analisi di dati e l’utilizzo di tecniche di data mining. Gli studenti impareranno come i classici algoritmi di data mining possono essere applicati sui Big Data usando Hadoop (Spark). Set di dati reali (e open source) verranno utilizzati per presentare esempi e per consentire agli studenti di costruire i propri progetti. Una metà delle lezioni consisterà in esercitazioni (laboratorio) e una metà delle lezioni sarà teorica.

  • Motivations: What is and Why Distributed Data Mining is needed in a Big Data Scenario
  • Recall parallel and distributed computing notions
  • Amdahl's law, differences between shared and distributed memory architectures
  • Introduction to Hadoop
  • Hadoop Ecosystem
  • Interacting with HDFS
  • Hadoop Combiners
  • Basic Spark and RDD
  • Map-Reduce Programming Patterns
  • Recall Python programming
  • Data Analysis with Spark
  • Data Mining and Machine Learning with Spark
  • Spark SQL
  • Spark Streaming
  • Example on how to prepare a project
Syllabus

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). Real (and open source) datasets will be used to present examples and to let the students build their own projects. Half of the lessons will consists of practice (Lab), and half of lectures.

  • Motivations: What is and Why Distributed Data Mining is needed in a Big Data Scenario
  • Recall parallel and distributed computing notions
  • Amdahl's law, differences between shared and distributed memory architectures
  • Introduction to Hadoop
  • Hadoop Ecosystem
  • Interacting with HDFS
  • Hadoop Combiners
  • Basic Spark and RDD
  • Map-Reduce Programming Patterns
  • Recall Python programming
  • Data Analysis with Spark
  • Data Mining and Machine Learning with Spark
  • Spark SQL
  • Spark Streaming
  • Example on how to prepare a project
Modalità d'esame

Students groups made of 2 o 3 students (max) develop a project (report + short slide presentation);
 
Assessment methods

Students groups made of 2 o 3 students (max) develop a project (report + short slide presentation);
Pagina web del corso

http://didawiki.di.unipi.it/doku.php/mds/ddam/start
Class web page

http://didawiki.di.unipi.it/doku.php/mds/ddam/start
Altri riferimenti web

Slides and materials on Microsoft Teams channel: https://bit.ly/3A8dH5V
Additional web pages

Slides and materials on Microsoft Teams channel: https://bit.ly/3A8dH5V
Updated: 15/11/2021 23:26