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Scheda Riassuntiva
Anno Accademico 2014/2015
Tipo incarico Dottorato
Insegnamento 096700 - A MULTIDISCIPLINARY PERSPECTIVE ON BIG DATA
Docente Della Valle Emanuele
Cfu 5.00 Tipo insegnamento Monodisciplinare


Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1300) - SCUOLA DI DOTTORATOAZZZZ096700 - A MULTIDISCIPLINARY PERSPECTIVE ON BIG DATA

Programma dettagliato e risultati di apprendimento previsti

LECTURERS

Responsible : Emanuele Della Valle

Lectures: Danilo Ardagna, Cinzia Cappiello, Paolo Ciuccarelli, Paolo Cremonesi, Emanuele Della Valle, Elisabetta Di Nitto, and Letizia Tanca

Invited Speakers: Fabrizio Antonelli (Telecom Italia), Stefano Ceri, Piero Fraternali, Marco Monti (IBM), and Marco Tagliasacchi

MISSION AND GOALS

The term Big Data refers to a growing torrent of information that, if successfully analyzed, can unleash new business opportunities and revenues. This course aims at introducing Big Data analytics methods and includes practical sessions on PoliMI’s Big Data computational infrastructure.

CLASSES

1) Introduction to big data - Emanuele Della Valle (2 hours)
   - Why now?
   - What is Big Data? volume, velocity, variety, veracity, …
   - Paradigm shifts enabled
   - Tools and Market Landscape

2) Infrastructures for big data problems to master the volume dimension - Paolo Cremonesi & Danilo Ardagna (4 hours)
   - Introduction to cloud computing
   - Technologies for Infrastructure-as-a-Service
   - Map Reduce, Hadop and Hadoop ecosystem
   - Map Reduce Cloud based solutions

3) Integrating and analyzing massive and heterogeneous (relational, graph, semi-structured) data collections and streams - Elisabetta Di Nitto, Letizia Tanca, and Emanuele Della Valle (4 hours)
   - Overview of NoSQL databases
   - Big data and NoSQL
   - mastering the variety dimension
   - mastering the velocity dimension

4) mastering the veracity dimension - Cinzia Cappiello (3 hours)
   - data quality
   - definition and dimensions
   - techniques for data quality assessment and improvement
   - uncertainty and data quality problems in big data"

5) Making sense of Big Data - Paolo Ciuccarelli, Letizia Tanca, and Paolo Cremonesi  (6 hours)
   - Big Data Visualisation
   - Big Data Exploration, summarisation and context-aware reduction
   - Recommender Systems

6) Open Workshop on Big Data (4 hours)
   - genomics applications - Stefano Ceri
   - cognitive computing - Marco Monti (IBM)
   - multimedia analytics - Piero Fraternali and Marco Tagliasacchi
   - Telecom Italia Big Data Challenge - Fabrizio Antonelli (Telecom Italia)
   - round table

7) Putting it all together on Telecom Italia Big Data Challenge dataset - Students (4 hours)
   - Assignments to students
   - Students’ reports on business value
   - Students’ reports on analytics
   - Students’ reports on visualisation

TEACHING MATERIALS

The course material consists in slides prepared by the lecturers, links to on-line tutorials, and the dataset of Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/bigdatachallenge/contest.html). Students will gain enough background on the topics to be able to use the infrastructure made available by IBM as well as the poliCloud one donated by Yahoo!.


Intervallo di svolgimento dell'attivitā didattica
Data inizio
Data termine

Calendario testuale dell'attivitā didattica
 

Bibliografia

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
23.0
esercitazione
0.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
60.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese

Note Sulla Modalitā di valutazione

The exams will consist in reporting the experience in using the tools and in discussing the different trade-offs offered by them. The reporting exercise will be organised in three tracks: one focus on business value, one on analytics and one on visualisation.


Note Docente
For more info about calendar and rooms go to: http://emanueledellavalle.org/Teaching/PhD-course-Big-Data.html
27/03/2017 Area Servizi ICT v. 1.2.1 / 1.2.1