In molti contesti applicativi sono in gioco enormi volumi di dati che
vengono utilizzati in ambito economico-finanziario, politico, sociale ed
anche istituzionale. Spesso i dati sono memorizzati in enormi cloud
distribuite e talvolta sono generati secondo un flusso continuo, così
consistente da renderne impossibile una memorizzazione completa. In
moltissimi casi i dati sono inerenti ad entità in fitta relazione tra
loro e danno luogo a immense reti di collegamenti. Esempi comuni di tali
reti sono le reti sociali e biologiche, le reti di distribuzione e il
grafo del Web. Inoltre il fatto che i dati siano memorizzati in sistemi
gestiti da terze parti pone problemi di integrità che non trovano
riscontro nella letteratura informatica classica sia per la tipologia
sia per la scala.
Questo scenario pone sfide algoritmiche inedite sulle quali è al lavoro
una vasta platea di ricercatori. Tale sforzo ha prodotto, nell’ultimo
decennio, molte novità sia sul piano metodologico sia sul piano
tecnologico. L’insegnamento ha lo scopo di trasferire agli studenti
alcuni tra i più importanti strumenti metodologici nati nell’ambito
della ricerca sugli algoritmi per Big Data. Tali strumenti metodologici
sono proposti assieme a contesti applicativi sfidanti.
vengono utilizzati in ambito economico-finanziario, politico, sociale ed
anche istituzionale. Spesso i dati sono memorizzati in enormi cloud
distribuite e talvolta sono generati secondo un flusso continuo, così
consistente da renderne impossibile una memorizzazione completa. In
moltissimi casi i dati sono inerenti ad entità in fitta relazione tra
loro e danno luogo a immense reti di collegamenti. Esempi comuni di tali
reti sono le reti sociali e biologiche, le reti di distribuzione e il
grafo del Web. Inoltre il fatto che i dati siano memorizzati in sistemi
gestiti da terze parti pone problemi di integrità che non trovano
riscontro nella letteratura informatica classica sia per la tipologia
sia per la scala.
Questo scenario pone sfide algoritmiche inedite sulle quali è al lavoro
una vasta platea di ricercatori. Tale sforzo ha prodotto, nell’ultimo
decennio, molte novità sia sul piano metodologico sia sul piano
tecnologico. L’insegnamento ha lo scopo di trasferire agli studenti
alcuni tra i più importanti strumenti metodologici nati nell’ambito
della ricerca sugli algoritmi per Big Data. Tali strumenti metodologici
sono proposti assieme a contesti applicativi sfidanti.
scheda docente materiale didattico
- 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.
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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 Adottati
Mining of Massive DatasetsJure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
Bibliografia Di Riferimento
Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/Modalità Erogazione
TradizionaleModalità Frequenza
Facoltativa.Modalità Valutazione
Prova scritta, valutazione progetto. La prova scritta puo' essere sostituita da prove in itinere parziali. scheda docente materiale didattico
- 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.
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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 Adottati
Mining of Massive DatasetsJure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
Bibliografia Di Riferimento
Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/Modalità Erogazione
Tradizionale.Modalità Frequenza
Facoltativa.Modalità Valutazione
Prova scritta, valutazione progetto. La prova scritta puo' essere sostituita da prove in itinere parziali. scheda docente materiale didattico
- 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.
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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 Adottati
Mining of Massive DatasetsJure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
Bibliografia Di Riferimento
Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/Modalità Erogazione
TradizionaleModalità Frequenza
FacoltativaModalità Valutazione
Prova scritta, valutazione progetto. La prova scritta può essere sostituita da prove in itinere parziali. scheda docente materiale didattico
- 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.
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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 Adottati
Mining of Massive DatasetsJure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
Modalità Valutazione
Prova scritta, valutazione progetto. La prova scritta puo' essere sostituita da prove in itinere parziali. scheda docente materiale didattico
- 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.
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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 Adottati
Mining of Massive DatasetsJure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
Bibliografia Di Riferimento
Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Cambridge University Press http://www.mmds.org/Modalità Erogazione
TradizionaleModalità Frequenza
FacoltativaModalità Valutazione
Prova scritta, valutazione progetto. La prova scritta puo' essere sostituita da prove in itinere parziali.