Dettagli sull'Insegnamento per l'A.A. 2019/2020
Nome:
Big Data: Models And Algorithms / Big Data: Models And Algorithms
Informazioni
Crediti:
: Master Degree in Computer Science 3 CFU (d)
Erogazione:
Master Degree in Computer Science 2nd anno curriculum NEDAS Elective
Lingua:
Inglese
![[info]](img/info16.png)
Prerequisiti
Basic courses on design and analysis of algorithms and data structures. Mathematical and programming maturity.
Fundamentals of data analysis.
Obiettivi
Upon completion of this course the student will have reliably demonstrated the ability to design, analyze and implement algorithms for massive data sets using
state-of-the-art algorithmic techniques in the area. Furthermore, the student will be able to understand: i) storage strategies that are suited for large-scale datasets (e.g. distributed, unstructured); ii)
alternative processing models that are relevant to big data; iii)
fundamentals of large-scale data mining.
Finally, the student will acquire basic knowledge of experimental algorithmics.
Sillabo
- Algorithmic Techniques and Computational Models for Processing Massive Datasets
- Scalable Storage frameworks and DBMSs, Distributed File Systems, Structured vs Unstructured Storage, NoSQL Paradigms
- Large Scale Data Mining (mining data streams, graph mining, text mining, data analytics)
- Experimental Algorithmics
Descrittori di Dublino
Alla fine del corso, lo studente dovrebbe
- Understand the challenges of large scale data mining
- Be able to describe in a comprehensible manner, analyze, evaluate, and compare the
performance of algorithms, with a focus on models of computation relevant to massive
data sets
- Be able to design and implement algorithms for computational problems at large scale through state-of-the-art techniques
- Be able to lookup and apply relevant research literature for problems related to storage and processing of massive data sets
- Be able to express oneself in writing at scientific level
- Know the foundations of the algorithmic experimental process design
Testi di riferimento
- Catherine McGeoch, A Guide to Experimental Algorithmics
- J. Leskovec, A. Rajaraman, J. D. Ullman, Mining of Massive Datasets. 2nd Edition
Modalità d'esame
Written Exam + Oral discussion (and/or Homework/Project)
Aggiornamenti alla pagina del corso
Le informazioni sulle editioni passate di questo corso sono disponibili per i seguenti anni accademici:
Per leggere le informazioni correnti sul corso, se ancora erogato, consulta il catalogo corsi di ateneo.
Ultimo aggiornamento delle informazioni sul corso: 28 novembre 2019, 10:10
English version
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