Insegnamento - Università Roma Tre

Algoritmi per big data (obiettivi)

Codice

20810211

Lingua

ITA

Tipo di attestato

Attestato di profitto

Crediti

6

Settore scientifico disciplinare

ING-INF/05

Ore Aula

54

Attività formativa

Attività formative caratterizzanti

Canale Unico

Docente	DI BATTISTA GIUSEPPE (programma) 1) Algorithms for data streams - Approximate counting - Majority problems - Sampling and reservoir sampling - Bloom filters - Frequent itemsets - Number of distinct elements 2) Dimensionality reduction Johnson–Lindenstrauss lemma Embedding metric spaces with low distortion 3) Algorithms and data structures for quantitative features analysis - orthogonal range searching (kd-trees and range trees) - nearest neighbour search, k-nearest neighbour search - fractional cascading and simplex range search 4) Algorithms for the decomposition of complex networks - Decomposition into k-connected components - Decomposition into k-cores, maximal cliques, maximal k-plexes 5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing 6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma. (testi) Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/
Date di inizio e termine delle attività didattiche	Dal 01/03/2022 al 10/06/2022
Modalità di erogazione	Tradizionale
Modalità di frequenza	Non obbligatoria
Metodi di valutazione	Prova scritta Valutazione di un progetto

Docente	PATRIGNANI MAURIZIO (programma) 1) Algorithms for data streams - Approximate counting - Majority problems - Sampling and reservoir sampling - Bloom filters - Frequent itemsets - Number of distinct elements 2) Dimensionality reduction - Johnson–Lindenstrauss lemma - Embedding metric spaces with low distortion 3) Algorithms and data structures for quantitative features analysis - orthogonal range searching (kd-trees and range trees) - nearest neighbour search, k-nearest neighbour search - fractional cascading and simplex range search 4) Algorithms for the decomposition of complex networks - Decomposition into k-connected components - Decomposition into k-cores, maximal cliques, maximal k-plexes 5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing 6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma. (testi) Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/
Date di inizio e termine delle attività didattiche	Dal 01/03/2022 al 10/06/2022
Modalità di erogazione	Tradizionale
Modalità di frequenza	Non obbligatoria
Metodi di valutazione	Prova scritta Valutazione di un progetto

Docente	FRATI FABRIZIO (programma) 1) Algorithms for data streams - Approximate counting - Majority problems - Sampling and reservoir sampling - Bloom filters - Frequent itemsets - Number of distinct elements 2) Dimensionality reduction - Johnson–Lindenstrauss lemma - Embedding metric spaces with low distortion 3) Algorithms and data structures for quantitative features analysis - orthogonal range searching (kd-trees and range trees) - nearest neighbour search, k-nearest neighbour search - fractional cascading and simplex range search 4) Algorithms for the decomposition of complex networks - Decomposition into k-connected components - Decomposition into k-cores, maximal cliques, maximal k-plexes 5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing 6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma. (testi) Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/
Date di inizio e termine delle attività didattiche	Dal 01/03/2022 al 10/06/2022
Modalità di erogazione	Tradizionale
Modalità di frequenza	Non obbligatoria
Metodi di valutazione	Prova scritta Valutazione di un progetto

Docente	PIZZONIA MAURIZIO (programma) 1) Algorithms for data streams - Approximate counting - Majority problems - Sampling and reservoir sampling - Bloom filters - Frequent itemsets - Number of distinct elements 2) Dimensionality reduction - Johnson–Lindenstrauss lemma - Embedding metric spaces with low distortion 3) Algorithms and data structures for quantitative features analysis - orthogonal range searching (kd-trees and range trees) - nearest neighbour search, k-nearest neighbour search - fractional cascading and simplex range search 4) Algorithms for the decomposition of complex networks - Decomposition into k-connected components - Decomposition into k-cores, maximal cliques, maximal k-plexes 5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing 6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma. (testi) Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/
Date di inizio e termine delle attività didattiche	Dal 01/03/2022 al 10/06/2022
Modalità di erogazione	Tradizionale
Modalità di frequenza	Non obbligatoria
Metodi di valutazione	Prova scritta Valutazione di un progetto

Docente	DA LOZZO GIORDANO (programma) 1) Algorithms for data streams - Approximate counting - Majority problems - Sampling and reservoir sampling - Bloom filters - Frequent itemsets - Number of distinct elements 2) Dimensionality reduction -Johnson–Lindenstrauss lemma Embedding metric spaces with low distortion 3) Algorithms and data structures for quantitative features analysis - orthogonal range searching (kd-trees and range trees) - nearest neighbour search, k-nearest neighbour search - fractional cascading and simplex range search 4) Algorithms for the decomposition of complex networks - Decomposition into k-connected components - Decomposition into k-cores, maximal cliques, maximal k-plexes 5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing 6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma. (testi) Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/
Date di inizio e termine delle attività didattiche	Dal 01/03/2022 al 10/06/2022
Modalità di erogazione	Tradizionale
Modalità di frequenza	Non obbligatoria
Metodi di valutazione	Prova scritta Valutazione di un progetto