Massimo CAFARO

Massimo CAFARO

Professore II Fascia (Associato)

Settore Scientifico Disciplinare ING-INF/05: SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI.

massimo.cafaro@unisalento.it

Dipartimento di Ingegneria dell'Innovazione

Centro Ecotekne Pal. O - S.P. 6, Lecce - Monteroni - LECCE (LE)

Ufficio, Piano terra

Telefono +39 0832 29 7371

Presidente del Consiglio Didattico in Ingegneria dell'Informazione. Direttore dell'unità di ricerca CINI. Afferente al Dipartimento di Ingegneria dell'Innovazione.

Area di competenza:

Calcolo parallelo e distribuito, grid/cloud computing, security, data mining, machine learning.

Orario di ricevimento

Previo appuntamento.

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Curriculum Vitae

Massimo Cafaro è professore associato presso il Dipartimento di Ingegneria dell'Innovazione dell'Università del Salento, e direttore della unità di ricerca CINI presso l'Università del Salento. Il suo ambito di ricerca riguarda Parallel e Distributed Computing, Grid e Cloud Computing, Data Mining e Big Data. E' Senior Member IEEE ed IEEE Computer Society, Senior Member ACM ed Associate Editor per la rivista IEEE Access. Laureato in Scienze dell'Informazione presso l’Università di Salerno ha conseguito il dottorato in Informatica presso l'Università di Bari. E' inoltre autore di oltre 100 pubblicazioni internazionali in refereed journals, proceedings e book chapters e co-autore del brevetto “Metodo e formalismo per inviare istruzioni a DataBase distribuiti realizzato mediante programma per computer”. E’ Visiting Professor presso il British Institute of Technology and E-Commerce, London, UK. È invited lecturer presso numerose università ed enti di ricerca, ed è membro dei Technical Committees IEEE on Parallel Processing, Distributed Processing, Scalable Computing e Services Computing. Inoltre, è Vice Chair of Regional Centers e coordinatore della Technical Area on Data Intensive Computing nell’ambito di IEEE Technical Committee on Scalable Computing. Collabora attivamente con il Centro Euro-Mediterraneano per i Cambiamenti Climatici (CMCC). Si occupa di aspetti di ricerca teorici e pratici prestando particolare attenzione  al design, analisi ed implementazione di algoritmi sequenziali, paralleli e distribuiti. L'attività scientifica include infine l'organizzazione di workshop internazionali e l'insegnamento in università italiane e straniere, ed in scuole di dottorato internazionali.


 

 

Didattica

A.A. 2018/2019

DATA MINING (ING-INF/05)

Corso di laurea MATEMATICA

Lingua INGLESE

Crediti 6.0

Anno accademico di erogazione 2018/2019

Per immatricolati nel 2018/2019

Struttura DIPARTIMENTO DI MATEMATICA E FISICA "ENNIO DE GIORGI"

Percorso PERCORSO COMUNE

ELEMENTI DI INFORMATICA (ING-INF/05)

Corso di laurea INGEGNERIA DELLE TECNOLOGIE INDUSTRIALI

Lingua ITALIANO

Crediti 6.0

Anno accademico di erogazione 2018/2019

Per immatricolati nel 2018/2019

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso unico

PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Lingua INGLESE

Crediti 9.0

Anno accademico di erogazione 2018/2019

Per immatricolati nel 2017/2018

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

A.A. 2017/2018

DATA MINING (ING-INF/05)

Corso di laurea MATEMATICA

Lingua INGLESE

Crediti 6.0

Anno accademico di erogazione 2017/2018

Per immatricolati nel 2017/2018

Struttura DIPARTIMENTO DI MATEMATICA E FISICA "ENNIO DE GIORGI"

Percorso PERCORSO COMUNE

PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Lingua INGLESE

Crediti 9.0

Anno accademico di erogazione 2017/2018

Per immatricolati nel 2016/2017

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

A.A. 2016/2017

MOBILE APPLICATIONS DEVELOPMENT (ING-INF/05)

Corso di laurea EUROPEAN HERITAGE,DIGITAL MEDIA AND THE INFORMATION SOCIETY

Crediti 6.0

Anno accademico di erogazione 2016/2017

Per immatricolati nel 2016/2017

Struttura DIPARTIMENTO DI BENI CULTURALI

Percorso INTERNAZIONALE

PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Crediti 9.0

Anno accademico di erogazione 2016/2017

Per immatricolati nel 2015/2016

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

A.A. 2015/2016

MOBILE APPLICATIONS DEVELOPMENT (ING-INF/05)

Corso di laurea EUROPEAN HERITAGE,DIGITAL MEDIA AND THE INFORMATION SOCIETY

Crediti 6.0

Anno accademico di erogazione 2015/2016

Per immatricolati nel 2015/2016

Struttura DIPARTIMENTO DI BENI CULTURALI

Percorso INTERNAZIONALE

PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Crediti 9.0

Anno accademico di erogazione 2015/2016

Per immatricolati nel 2014/2015

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

A.A. 2014/2015

PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Crediti 9.0

Anno accademico di erogazione 2014/2015

Per immatricolati nel 2013/2014

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Torna all'elenco
DATA MINING (ING-INF/05)

Corso di laurea MATEMATICA

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2018/2019

Anno accademico di erogazione 2018/2019

Anno 1

Semestre Secondo Semestre (dal 25/02/2019 al 31/05/2019)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Calculus. Probability and Statistics. Linear Algebra. Programming skills.

The course provides a modern introduction to data mining, which spans techniques, algorithms and methodologies for discovering structure, patterns and relationships in data sets (typically, large ones) and making predictions. Applications of data mining are already happening all around us, and, when they are done well, sometimes they even go unnoticed. For instance, how does the Google web search work? How does Shazam recognize a song? How does Netflix recommend movies to its users? The principles of data mining provide answers to these and others questions. Data mining overlaps the fields of computer science, statistical machine learning and data bases. The course aims at providing the students with the knowldedge required to explore, analyze and leverage available data in order to turn the data into valuable and actionable information for a company, for instance, in order to facilitate a decision-making process.

Knowledge and understanding. The course describes methods and models for the analysis of large amounts of data. Students must have a solid background with a broad spectrum of basic knowledge related to data mining:

  • the students must have the basic cognitive tools to think analytically, creatively, critically and in an inquiring way, and have the abstraction and problem-solving skills needed to cope with complex systems;
  • they must have solid knowledge of data mining models and methodologies;
  • they must be able to work on large data collections, including heterogeneous and produced at high speed data, in order to integrate them - in particular by knowing how to manage their origin and quality - and to carry out in-depth thematic analyses, drawing on this knowledge to improve the decision-making process.

Applying knowledge and understanding. After the course the student should be able to:

  • describe and use the main data mining techniques;
  • understand the differences among several algorithms solving the same problem and recognize which one is better under different conditions;
  • tackle new data mining problems by selecting the appropriate methods and justifying his/her choices;
  • tackle new data mining problems by designing suitable algorithms and evaluating the results;
  • explain experimental results to people outside of statistical machine learning or computer science.

Making judgements. Students must have the ability to process complex and/or fragmentary data and must arrive at original and autonomous ideas and judgments, and consistent choices in the context of their work, which are particularly delicate in the profession of data scientist. The course promotes the development of independent judgment in the appropriate choice of technique/model for data processing and the critical ability to interpret the goodness of the results of the models/methods applied to the datasets under examination.

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired and their scientific knowledge and, in particular, the specialty vocabulary. Students should be able to organize effective dissemination and study material through the most common presentation tools, including computer-based ones, to communicate the results of data analysis processes, for example by using visualization and reporting tools aimed at different types of audiences.

Learning skills. Students must acquire the critical ability to relate, with originality and autonomy, to the typical problems of data mining and, in general, cultural issues related to other similar areas. They should be able to develop and apply independently the knowledge and methods learnt with a view to possible continuation of studies at higher (doctoral) level or in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students should be able to switch to exhibition forms other than the source texts in order to memorize, summarize for themselves and for others, and disseminate scientific knowledge.

The course aims to provide students with advanced tools for data analysis, through which to extrapolate relevant information from large datasets and guide the related decision-making processes. The course consists of frontal lessons using slides made available to students via the Moodle platform, and classroom exercises. The frontal lessons are aimed at improving students' knowledge and understanding through the presentation of theories, models and methods; students are invited to participate in the lesson with autonomy of judgement, by asking questions and presenting examples. The exercises are aimed at understanding the algorithms and models presented.

Oral exam. During the exam the student is asked to illustrate theoretical topics in order to verify his/her knowledge and understanding of the selected topics. The student must demonstrate adequate knowledge and understanding of the issues presented or indicated, applying in a relevant manner the theories and conceptual models covered by the study programme.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Programma esteso

Introduction. Map-Reduce. Mining data streams. Frequent Items.  Frequent Itemsets and association rules. Mining similar items and Locality-Sensitive Hashing. Graph analysis. Link analysis and PageRank. Clustering. Recommendation systems. Mining Social-Network Graphs.  Dimensionality reduction.  Classification.

Mining of Massive Datasets 

J. Leskovec, A. Rajaraman and J. Ullman

Freely availableonline: http://www.mmds.org

 

Data Mining and Analysis

M. J. Zaki and W. Meira

Freely available online: http://dataminingbook.info

DATA MINING (ING-INF/05)
ELEMENTI DI INFORMATICA (ING-INF/05)

Corso di laurea INGEGNERIA DELLE TECNOLOGIE INDUSTRIALI

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2018/2019

Anno accademico di erogazione 2018/2019

Anno 1

Semestre Secondo Semestre (dal 04/03/2019 al 04/06/2019)

Lingua ITALIANO

Percorso unico (A96)

ELEMENTI DI INFORMATICA (ING-INF/05)
PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2017/2018

Anno accademico di erogazione 2018/2019

Anno 2

Semestre Primo Semestre (dal 24/09/2018 al 21/12/2018)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

Calculus I and II, Probability Theory. Programming skills and working knowledge of the C programming language.

The course provides a modern introduction to design, analysis and implementation of sequential and parallel algorithms. In particular, the course is based on a pragmatic approach to parallel programming of message-passing algorithms through the C language and the MPI library.

Knowledge and understanding. Students must have a solid background with a broad spectrum of basic knowledge of sequential and parallel algorithms:

 

· the students must have the basic cognitive tools to think analytically, creatively, critically and in an inquiring way, and have the abstraction and problem-solving skills needed to cope with complex systems;

· they must have a solid knowledge of the design and implementation of sequential and parallel efficient algorithms;

· they must have the tools for analysing the resources used by algorithms;

· they must have a catalogue of the most well-known and efficient sequential and parallel algorithms for basic computational problems.

 

Applying knowledge and understanding. After the course the student should be able to:

 

· Describe and use the main design techniques for sequential algorithms;

· Design, prove the correctness and analyze the computational complexity of sequential algorithms;

· Understand the differences among several algorithms solving the same problem and recognize which one is better under different conditions;

· Describe and use basic sequential algorithms;

· Describe and use basic data structures; know about the existence of advanced data structures;

· Understand the difference between sequential and parallel algorithms;

· Design, implement and analyze message-passing based parallel algorithms in C using the MPI library;

· Describe and use basic parallel algorithms.

 

 

Making judgements. Students are guided to learn critically everything that is explained to them in class, to compare different approaches to solving algorithmic problems, and to identify and propose, in an autonomous way, the most efficient solution they find.

 

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired and their scientific knowledge and, in particular, the specialty vocabulary. The course promotes the development of the following skills of the student: ability to expose in precise and formal terms an abstract model of concrete problems, identifying the salient features of them and discarding the nonessential ones; ability to describe and analyze an efficient solution to the problem in question.

 

Learning skills. Students must acquire the critical ability to relate, with originality and autonomy, to the typical problems of data mining and, in general, cultural issues related to other similar areas. They should be able to develop and apply independently the knowledge and methods learnt with a view to possible continuation of studies at higher (doctoral) level or in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students should be able to switch to exhibition forms other than the source texts in order to memorize, summarize for themselves and for others, and disseminate scientific knowledge.

The course aims to enable students to abstract formal algorithmic models and problems from concrete computational problems, and to design efficient algorithmic solutions for them. This will be done using the following teaching method. Every computational problem will be introduced, motivating it with concrete examples. The presentation of each topic will be divided into four parts: 1. Description of the actual computational problem. 2. Modelling the real problem by means of an abstract problem. 3. Resolution of the abstract problem through an algorithm obtained through the application of the general techniques of design of algorithms introduced in the course. 4. Analysis of the resources used by the algorithm. The course consists of frontal lessons using slides made available to students via the Moodle platform, and classroom exercises. There will be theoretical lessons aimed at learning the basic techniques for the project and analysis of algorithms, and a part of lessons of an exercise type in which you will illustrate, with plenty of examples, how the theoretical knowledge acquired can be used in order to solve algorithmic problems of practical interest and implement parallel algorithms in C language through the MPI library.

Oral exam. Optionally, a student may be assigned a small project. During the exam the student is asked to illustrate theoretical topics in order to verify his/her knowledge and understanding of the selected topics. The student may also be asked to design a very simple algorithm in order to assess his/her ability to identify and use the relevant design techniques; alternatively, the student may be asked to analyze the complexity of a small code fragment.

Office Hours

By appointment; contact the instructor by email or at the end of class meetings.

Programma esteso

Sequential Algorithms

 

Introduction. Order of growth. Analysis of algorithms. Decrease and conquer.  Divide and conquer. Recurrences.  Randomized algorithms.  Transform and conquer.  Dynamic programming.  Greedy algorithms. Complexity and computability. NP-Completeness. 

 

Parallel Algorithms

Introduction. The transition from sequential to parallel computing. Parallel complexity.  Parallel architectures.  Parallel algorithm design.  Message-Passing programming.  Sieve of Erathostenes.  Floyd all-pairs shortest path algorithm. Performance analysis.  Matrix-vector multiplication. Document classification.  Matrix multiplication.

Introduction to Algorithms. Third edition. Cormen, Leiserson, Rivest, Stein. The MIT Press

Parallel Programming in C with MPI and OpenMP International Edition (2004) Michael J. Quinn McGraw-Hill

PARALLEL ALGORITHMS (ING-INF/05)
DATA MINING (ING-INF/05)

Corso di laurea MATEMATICA

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2017/2018

Anno accademico di erogazione 2017/2018

Anno 1

Semestre Secondo Semestre (dal 26/02/2018 al 25/05/2018)

Lingua ITALIANO

Percorso PERCORSO COMUNE (999)

Analisi Matematica. Probabilità e statistica. Algebra lineare. Programmazione ed analisi di algoritmi.

Introduzione al corso. Map-Reduce. (2 ore) Mining data streams. Frequent Items. (6 ore)  Frequent Itemsets ed association rules.(4 ore)  Mining similar items e Locality-Sensitive Hashing. (2 ore)  Analisi di grafi. Link analysis e PageRank. (2 ore)  Clustering. (4 ore)  Recommendation systems. (4 ore)  Mining Social-Network Graphs. (4 ore)  Dimensionality reduction. (2 ore)  Classification. (6 ore) Esercitazioni. (6 ore).

Il corso fornisce una moderna introduzione al data mining, un insieme di tecniche, algoritmi e metodologie per scoprire la struttura, patterns e relazioni in insiemi di dati (tipicamente, quelli più grandi) e fare previsioni. Le applicazioni del data mining stanno già accadendo intorno a noi, e se ben fatte, possono a volte anche passare inosservate. Come funziona la ricerca sul web di Google? Come fa Shazam a riconoscere una canzone? Come fa Netflix a raccomandare film a ciascuno dei suoi utenti? I principi del data mining forniscono le risposte di base a queste e ad altre domande simili. Il data mining abbraccia i campi dell’informatica, dello statistical machine learning e dei database. Obiettivo del corso è mettere in grado gli studenti di esplorare, analizzare e sfruttare i dati disponibili al fine di trasformarli in informazioni quantitative e qualitative di valore ed interesse per una azienda, ad esempio ai fini di un processo di decision-making.

Risultati di apprendimento

Dopo aver seguito il corso, lo studente dovrebbe essere in grado di:

 

•        descrivere ed utilizzare le principali tecniche di data mining;

•        comprendere le differenze tra algoritmi diversi che risolvono uno stesso problema e riconoscere quale algoritmo è il migliore rispetto a condizioni diverse;

•        affrontare nuovi problemi di data mining scegliendo i metodi più appropriati e giustificando le proprie scelte;

•        affrontare nuovi problemi di data mining progettando appositi algoritmi e valutando i risultati;

•        spiegare i risultati ottenuti sperimentalmente anche a persone con un background teorico diverso da statistica e/o informatica.

L’esame è orale. Durante l’esame, allo studente viene chiesto di illustrare argomenti teorici per verificare la sua conoscenza e comprensione degli argomenti scelti. 

Mining of Massive Datasets 

J. Leskovec, A. Rajaraman and J. Ullman

Disponibile gratuitamente online: http://www.mmds.org

 

Data Mining and Analysis

M. J. Zaki and W. Meira

Disponibile gratuitamente online: http://dataminingbook.info

DATA MINING (ING-INF/05)
PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2016/2017

Anno accademico di erogazione 2017/2018

Anno 2

Semestre Primo Semestre (dal 25/09/2017 al 22/12/2017)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

Analisi I e II, Teoria della Probabilita’. Conoscenze pregresse del linguaggio di programmazione C.

Algoritmi sequenziali

 

Introduzione. Ordini di grandezza. Analisi di algoritmi. Decrease and conquer. Divide and conquer. Ricorrenze. (7 ore) Algoritmi randomizzati. (5 ore)  Transform and conquer. (2 ore)  Lower bound for sorting. Linear time sorting algorithms: Counting sort, Radix sort and Bucket sort. (3 ore) Order statistics. Selection in expected and worst case linear time. (2 ore) Programmazione dinamica. (4 ore) Algoritmi greedy. (3 ore) Complessita’ e calcolabilita’. NP-Completezza. (7 ore)

 

Algoritmi paralleli

 

1) Introduzione. Metodo scientifico moderno. Concorrenza e parallelismo. Equazioni di Bernstein. Evoluzione del supercalcolo. Calcolatori paralleli. Grafo delle dipendenze dei dati. Data parallelism. Functional Parallelism. Pipelining. Approcci per la programmazione di calcolatori paralleli. (2 ore)

 

2) Architetture parallele. Reti di interconnessione. Shared e switched media. Topologie di rete. 2D mesh. Binary tree. Hypertree. Fat tree. Butterfly. Ipercubo. Torus. Shuffle-exchange. Processor arrays. Multiprocessori centralizzati e distribuiti. Multicomputers asimmetrici e simmetrici. Clusters e reti di workstations. Tassonomia di Flinn. (5 ore)

 

3) Parallel algorithm design. Modello task-channel. Metodologia PCAM di Foster. Decomposizioni, tasks e grafo delle dipendenze dei tasks. Grain size di una decomposizione. Grado di concorrenza. Critical path length. Grafo delle interazioni dei tasks. Processi e mapping. Recursive decomposition, Data decomposition Exploratory decomposition, Speculative decomposition. Decomposizione di dati di input, intermedi e di output. Decomposizione ibride e gerarchiche. The Owner Computes Rule. Tasks static e dinamici. Comunicazione locale, globale, strutturata e non strutturata. Task interactions: read-only, read-write, one-way, two-way. Comunicazione static e dinamica, sincrona ed asincrona. Algoritmi red-black.  Divide and conquer. Agglomerazione. Effetto surface to volume. Communication/computation ratio. Replicare la computazione per eliminare comunicazioni. Mapping. NP-completeness del mapping. Mapping static e dinamico.     Mappings based on data partitioning. Mappings based on task graph partitioning. Hybrid mappings. Probabilistic mapping, cyclic and block-cyclic. Graph partitioning. Recursive bisection. Quad and Oct-trees. Space-filling curves. Scattered decomposition. Dynamic load-balancing. Centralized dynamic mapping, master-slave (manager-worker), chunck scheduling. Distributed dynamic mapping. Dynamic data driven mapping. Dynamic geometric decomposition: Adaptive Mesh Refinement (AMR). Esempi di progettazione di algoritmi paralleli: boundary value problem, reductions, n-body problem. Data input. (10 ore)

 

 

4) Message-Passing programming. Message-passing model. MPI. Circuit-SAT problem. Communicators. Point-to-point communications: vari tipi di send e receive. Collective communications: broadcast, reductions, scatter, gather, all-gather,  all-to-all. Benchmark. (2 ore)

 

5) Crivello di Eratostene: progettazione, analisi, implementazione e benchmark. (2 ore)

 

6) Algoritmo di Floyd all-pairs shortest path: progettazione, analisi, implementazione e benchmark. (2 ore)

 

7) Performance analysis. Tempo di esecuzione sequenziale e parallelo. Communication overhead. Overhead totale. Speedup ed efficienza. Speedup relativo, reale, assoluto, relae asintotico e relativo asintotico. Cost-normalized speedup. Numero ottimale di processori. Speedup superlineare. Legge di Amdahl-Ware. Legge di  Gustafson-Barsis. Metrica di Karp-Flatt. Legge di Lee (generalizzazione della legge di Amdahl-Ware). Metrica di isoefficienza (Grama, Gupta and Kumar). Funzione di scalabilita’ di Sun e Ni. Cost-optimality (work-efficiency). Cost-optimality ed isoefficienza. Grado di concorrenza ed isoefficienza. Impatto della non cost-optimality di un algoritmo parallelo. Minimum Parallel execution Time. Minimum Cost-Optimal Parallel Execution Time. (7 ore)

 

8) Moltiplicazione matrice-vettore. Algoritmo sequenziale. Rowwise block-striped decomposition. Columnwise block-striped decomposition. Checkerboard block decomposition. implementazione e benchmark. (3 ore)

 

 

9) Classificazione di documenti. Design. Manager-worker paradigm. Comunicazioni nonblocking. (2 ore)

 

10) Moltiplicazione di matrici. Algoritmo sequenziale iterativo row-oriented e ricorsivo block-oriented.  Rowwise block-striped  parallel algorithm. Algoritmo di Cannon. Algoritmo di Fox. Algoritmo DNS (dei tre indiani). (3 ore)

 

Esercitazioni. (21 ore)

Il corso fornisce una moderna introduzione alla progettazione, analisi ed implementazione di algoritmi sequenziali e paralleli. In particolare, il corso e’ basato su un approccio pratico alla programmazione parallela di algoritmi message-passing in linguaggio C con la libreria MPI.

 

Dopo aver seguito il corso, lo studente dovrebbe essere in grado di:

 

1)      descrivere ed utilizzare le principali tecniche di progettazione di algoritmi sequenziali;

2)      progettare, provare la correttezza, implementare ed analizzare la complessita’ computazionale di algoritmi sequenziali;

3)      comprendere le differenze tra algoritmi diversi che risolvono uno stesso problema e riconoscere quale algoritmo e’ il migliore rispetto a condizioni diverse;

4)      descrivere ed utilizzare algoritmi sequenziali di base;

5)      descrivere ed utilizzare strutture dati di base; conoscere l’esistenza di strutture dati avanzate;

6)      comprendere le differenze tra algoritmi sequenziali e paralleli;

7)      progettare, provare la correttezza, implementare ed analizzare la complessita’ computazionale di algoritmi paralleli basati su message-passing in C  utilizzando la libreria MPI;

8)      descrivere ed utilizzare algoritmi paralleli di base.

L’esame e’ orale. Opzionalmente, allo studente puo’ essere assegnato un piccolo progetto. Duramte l’esame, allo studente viene chiesto di illustrare argomenti teorici per verificare la sua conoscenza e comprensione degli argomenti scelti. Allo studente puo’ essere chiesto di progettare un semplice algoritmo per verificare la sua capacita’ di identificare ed utilizzare le tecniche di progettazione piu’ appropriate. Alternativamente, allo studente puo’ essere chiesto di analizzare la complessita’ computazionale di un breve frammento di codice.

PARALLEL ALGORITHMS (ING-INF/05)
MOBILE APPLICATIONS DEVELOPMENT (ING-INF/05)

Corso di laurea EUROPEAN HERITAGE,DIGITAL MEDIA AND THE INFORMATION SOCIETY

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2016/2017

Anno accademico di erogazione 2016/2017

Anno 1

Semestre Primo Semestre (dal 26/09/2016 al 20/01/2017)

Lingua INGLESE

Percorso INTERNAZIONALE (A56)

MOBILE APPLICATIONS DEVELOPMENT (ING-INF/05)
PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2015/2016

Anno accademico di erogazione 2016/2017

Anno 2

Semestre Primo Semestre (dal 26/09/2016 al 22/12/2016)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

PARALLEL ALGORITHMS (ING-INF/05)
MOBILE APPLICATIONS DEVELOPMENT (ING-INF/05)

Corso di laurea EUROPEAN HERITAGE,DIGITAL MEDIA AND THE INFORMATION SOCIETY

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2015/2016

Anno accademico di erogazione 2015/2016

Anno 1

Semestre Secondo Semestre (dal 22/02/2016 al 21/05/2016)

Lingua INGLESE

Percorso INTERNAZIONALE (A56)

MOBILE APPLICATIONS DEVELOPMENT (ING-INF/05)
PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2014/2015

Anno accademico di erogazione 2015/2016

Anno 2

Semestre Primo Semestre (dal 21/09/2015 al 18/12/2015)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

PARALLEL ALGORITHMS (ING-INF/05)
PARALLEL ALGORITHMS (ING-INF/05)

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare ING-INF/05

Anno accademico 2013/2014

Anno accademico di erogazione 2014/2015

Anno 2

Semestre Primo Semestre (dal 29/09/2014 al 13/01/2015)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

PARALLEL ALGORITHMS (ING-INF/05)

Gli studenti interessati possono chiedere una tesi relativa al data mining sequenziale, parallelo e distribuito. In alternativa, gli studenti possono anche chiedere una tesi di laurea in materia di sicurezza e crittografia. Infine, la tesi può anche essere strettamente correlata al calcolo parallelo e distribuito, inclusi cloud, P2P e internet of things.

Pubblicazioni

 

Edited books

 

[2] Grids, Clouds and Virtualization, Springer-Verlag, London, 2011, Series: Computer Communications and Networks. Edited by Massimo Cafaro and Giovanni Aloisio. Hardcover ISBN 978-0-85729-048-9, Softcover ISBN 978-1-4471-2592-1, eBook ISBN 978-0-85729-049-6

[1] Workshop Proceedings of the Conference Grid and Pervasive Computing 2009, May 4-8 2009, Geneva, Switzerland, IEEE, edited by H. Muller, J. Chen, M. Cafaro, J. H. Park and N. Abdennadher, ISBN 978-0-7695-3677-4

 

Journals

 

[35] M.Cafaro, P. Pelle’, “Space-efficient Verifiable Secret Sharing Using Polynomial Interpolation”, IEEE Transactions on Cloud Computing,  IEEE, vol. 6, no. 2, pp. 453-463, April-June 2018, DOI 10.1109/TCC.2015.2396072, ISSN: 2168-7161, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7018923&isnumber=8372844

[34] M. Cafaro, M. Pulimeno, I. Epicoco, "Parallel mining of time-faded heavy hitters", Expert Systems With Applications, Elsevier, Volume 96, 2018, Pages 115-128, ISSN: 0957-4174, DOI: 10.1016/j.eswa.2017.11.021

[33] M. Cafaro, I. Epicoco, M. Pulimeno, G. Aloisio, "On Frequency Estimation and Detection of Frequent Items in Time Faded Streams”, IEEE Access, IEEE, Volume 5, Issue 1, (2017), pp. 24078-24093, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2017.2757238

[32] I.Fast and Accurate Mining of Correlated Heavy Hitters”, Data Mining & Knowledge Discovery, Springer, Volume 32, Issue 1, 2018, pp. 162-186, ISSN: 1384-5810, DOI:10.1007/s10618-017-0526-x

[31] Arcangelo Messina, Massimo Cafaro. "Parallel damage detection through finite frequency changes on multicore processors", Structural Engineering and Mechanics, Techno-Press, Volume 63, No. 4 (2017), pp. 457-469. ISSN: 1225-4568, DOI: https://doi.org/10.12989/sem.2017.63.4.457

[30] M. Cafaro, M. Pulimeno, I. Epicoco, G. Aloisio, “Parallel Space Saving on Multi and Many-Core Processors”, Concurrency and Computation: Practice and Experience, John Wiley and Sons Ltd, Volume 30, Issue 7, DOI: 10.1002/cpe.4160, Print ISSN 1532-0626, Online ISSN 1532-0634

[29] M. Cafaro, M. Pulimeno, I. Epicoco, G. Aloisio, “Mining frequent items in the time fading model”, Information Sciences, Elsevier, Volume 370-371, 2016, pp. 221-238, ISSN: 0020-0255, DOI: 10.1016/j.ins.2016.07.077 available on ScienceDirect http://www.sciencedirect.com/science/article/pii/S0020025516305618 and arXiv https://arxiv.org/abs/1601.03892

[28] M. Cafaro, M. Pulimeno, P. Tempesta, “A Parallel Space Saving Algorithm For Frequent Items and the Hurwitz zeta distribution”, Information Sciences, Elsevier, Volume 329, 2016, pp. 1 - 19, ISSN: 0020-0255, DOI: 10.1016/j.ins.2015.09.003 available on ScienceDirect http://www.sciencedirect.com/science/article/pii/S002002551500657X and arXiv http://arxiv.org/abs/1401.0702

[27] M. Cafaro, R. Civino, B. Masucci, "On the Equivalence of Two Security Notions for Hierarchical Key Assignment Schemes in the Unconditional Setting", IEEE Transactions on Dependable and Secure Computing, Volume 12, Issue 4 (2015),  pp. 485 - 490, IEEE, DOI 10.1109/TDSC.2014.2355841, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6894147, ISSN: 1545-5971

[26] M. Cafaro, M. Mirto, G. Aloisio, "Preference-Based Matchmaking of Grid Resources with CP-Nets", Journal of Grid Computing, Volume 11, Issue 2 (2013), pp. 211-237, Springer Netherlands, DOI: 10.1007/s10723-012-9235-2 Print ISSN 1570-7873, Online ISSN 1572-9184

[25] M. Cafaro, P. Tempesta: Finding frequent items in parallel. Concurrency and Computation: Practice and Experience Volume 23 Issue 15 pp. 1774-1788 (2011), John Wiley and Sons Ltd., Chichester, UK DOI: 10.1002/cpe.1761 Print ISSN 1532-0626, Online ISSN 1532-0634

[24] M. Cafaro, H. Müller, N. Abdennadher: Special Section: Grid and Pervasive Computing 2009. Future Generation Computer Systems Volume 27, Issue 5, May 2011, pp. 587-589,  Elsevier, DOI:10.1016/j.future.2010.11.019, ISSN 0167-739X

[23] J. Chen, M. Cafaro: Special Issue: 3rd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2008). Concurrency and Computation: Practice and Experience Volume 21 Issue 16 pp. 1961-1964 (2009) John Wiley and Sons Ltd., DOI: 10.1002/cpe.1459, Print ISSN 1532-0626, Online ISSN 1532-0634

[22] M. Cafaro, V. De Bene, G. Aloisio, “Deterministic Parallel Selection Algorithms on Coarse Grained Multicomputers”, Concurrency and Computation: Practice and Experience, Volume 21 Issue 18 (2009), pp. 2336–2354, John Wiley and Sons Ltd., DOI: 10.1002/cpe.1453, Print ISSN 1532-0626, Online ISSN 1532-0634

[21] M. Cafaro, I. Epicoco, S. Fiore, D. Lezzi, S. Mocavero, G. Aloisio, “Near Real-Time Parallel Processing and Advanced Data Management of SAR Images in Grid Environments”, Journal of Real-Time Image Processing, Special issue on architectures and techniques for real-time processing of remotely sensed images, Volume 4, Number 3, 2009, pp. 219-227, Springer-Verlag, DOI 10.1007/s11554-009-0119-z, Print ISSN 1861-8200, Online ISSN 1861-8219

[20] M. Cafaro, I. Epicoco, M. Mirto, D. Lezzi, G. Aloisio, "The Grid Resource Broker Workflow Engine", Concurrency and Computation: Practice and Experience, Wiley, Special Issue: 2nd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2007), Volume 20, Issue 15, pp. 1725 – 1739, 2008, John Wiley & Sons, Ltd., DOI: 10.1002/cpe.1318, Print ISSN 1532-0626, Online ISSN 1532-0634

[19] M. Mirto, S. Fiore, I. Epicoco, M. Cafaro, S. Mocavero, E. Blasi, G. Aloisio, "A Bioinformatics Grid Alignment Toolkit", Future Generation Computer Systems, Elsevier, Volume 24, Number 7, pp. 752-762, 2008, DOI:10.1016/j.future.2008.02.001, ISSN 0167-739X

[18] M. Mirto, M. Cafaro, S. Fiore, Daniele Tartarini, G. Aloisio, "A Grid-enabled Protein Secondary Structure Predictor", IEEE Transactions on NanoBioscience,  IEEE, Volume 6, Issue 2, pp. 124-130, June 2007, DOI 10.1109/TNB.2007.897475, ISSN 1536-1241

[17] G. Aloisio, M. Cafaro, G. Carteni, I. Epicoco, S. Fiore, D. Lezzi, M. Mirto , S. Mocavero, "The Grid Resource Broker Portal", Concurrency and Computation: Practice and Experience, Special Issue on Grid Computing Environments, Volume 19, Issue 12 (2007), pp. 1663-1670, John Wiley & Sons, Ltd., DOI: 10.1002/cpe.1131, Print ISSN 1532-0626, Online ISSN 1532-0634

[16] Y. Yang, O. F. Rana, D.W. Walker, R. Williams, C. Georgousopoulos, M. Cafaro, G. Aloisio, "An agent infrastructure for on-demand processing of remote-sensing archives", Int. J. on Digital Libraries Volume 5, Issue 2, pp 120-132 (2005), Springer-Verlag, DOI 10.1007/s00799-003-0054-8, Print ISSN 1432-5012, Online ISSN 1432-1300

[15] G. Aloisio, M.C. Barba, M. Cafaro, E. Blasi, S. Fiore, M. Mirto, "A Web Service based Grid Portal for Edgebreaker Compression", Methods of Information in Medicine, Special issue on HealthGrid Volume 44 Issue 2, pp. 233-238, Schattauer Publishers (2005), ISSN 0026-1270

[14] G. Aloisio, M. Cafaro, G. Carteni, I. Epicoco, G. Quarta (2005). A grid portal for Earth Observation community. IL NUOVO CIMENTO DELLA SOCIETÀ ITALIANA DI FISICA C, GEOPHYSICS AND SPACE PHYSICS, Società Italiana di Fisica, Bologna, vol. 28 issue 2, pp. 193-203 (2005), DOI: 10.1393/ncc/i2005-10182-5, Print ISSN 2037-4909, Online ISSN 1826-9885

[13] G.P. Marra, I. Schipa, G. Aloisio, M. Cafaro, D. Conte, C. Elefante, C. Mangia, M. Miglietta, U. Rizza, A. Tanzarella (2005). G-AQFS (Grid Air Quality Forecast System): An experimental system based on GRID computing technologies to forecast atmospheric dispersion of pollutants. IL NUOVO CIMENTO DELLA SOCIETÀ ITALIANA DI FISICA. C, GEOPHYSICS AND SPACE PHYSICS, Società Italiana di Fisica, Bologna, vol. 28 issue 2, pp. 183-192 (2005), Print ISSN 2037-4909, Online ISSN 1826-9885

[12] G. Aloisio, M. Cafaro, S. Fiore, I. Epicoco, M. Mirto, S. Mocavero, “Performance Analysis of Information Services in a Grid Environment”, Journal of Systemics, Cybernetics and Informatics, Volume 2, Number 5, 2004, pp. 24-30, Online ISSN 1690-4524

[11] G. Aloisio, M.C. Barba, E. Blasi, M. Cafaro, S. Fiore, M. Mirto, “BIG: a Grid Portal for Biomedical Data and Images”, Journal of Systemics, Cybernetics and Informatics, Volume 2, Number 3, 2004, pp. 10-18, Online ISSN 1690-4524

[10] G. Aloisio, M. Cafaro, R. Cesari, C. Mangia, G.P. Marra, M. Miglietta, M. Mirto, U. Rizza, I. Schipa, A. Tanzarella, "G-AQFS: Grid computing exploitation for the management of air quality in presence of complex meteorological circulations", Journal of Digital Information Management, Volume 2, Number 2, pp. 67-73, June 2004, Digital Information Research Foundation (DIRF) Press, ISSN 0972-7272

[9] G. Aloisio, M. Cafaro, M. Mirto, S. Fiore, "Early Experiences with the GRelC Library", Journal of Digital Information Management, Volume 2, Number 2, pp. 54-60, June 2004, Digital Information Research Foundation (DIRF) Press, ISSN 0972-7272

[8] G. Aloisio, M. Cafaro, "A Dynamic Earth Observation System", Parallel Computing, Volume 29, Issue 10 (2003), pp. 1357-1362, Special Issue on High performance computing with geographical data, Elsevier, DOI:10.1016/j.parco.2003.04.002, ISSN 0167-8191

[7] G. Aloisio, M. Cafaro, D. Lezzi, "The Desktop Grid Environment Enabler", Computing and Informatics, Volume 21, Number 4 (2002), pp. 333-345, Special Issue on Grid Computing, Slovak Academy of Sciences, Institute of Informatics, ISSN 1335-9150

[6] G. Aloisio, M. Cafaro, I. Epicoco, "Early experiences with the GridFTP protocol using the GRB-GSIFTP library", Future Generation Computer Systems, Volume 18, Issue 8 (2002), pp. 1053-1059, Special Issue on Grid Computing: Towards a New Computing Infrastructure, North-Holland, DOI:10.1016/S0167-739X(02)00084-5, ISSN 0167-739X

[5] G. Aloisio, M. Cafaro, "Web-based access to the grid using the Grid Resource Broker Portal", Concurrency and Computation: Practice and Experience, Volume 14 Issue 13-15 (2002), pp. 1145-1160, Special Issue on Grid Computing Environments, John Wiley & Sons, Ltd., DOI: 10.1002/cpe.677, Print ISSN 1532-0626, Online ISSN 1532-0634

[4] G. Aloisio, M. Cafaro, E. Blasi, I. Epicoco, "The Grid Resource Broker, a Ubiquitous Grid Computing Framework", Journal of Scientific Programming, Volume 10, Number 2 (2002), pp. 113-119, Special Issue on Grid Computing, IOS Press, Amsterdam, Print ISSN 1058-9244, Online ISSN 1875-919X

[3] G. Aloisio, M. Cafaro, C. Kesselman, R. Williams, "Web Access to SuperComputing", IEEE Computing in Science and Engineering, IEEE, Volume 3, Issue 6 (2001), pp. 66-72, DOI 10.1109/5992.963430, ISSN 1521-9615

[2] G. Aloisio, M.Cafaro, P. Beraldi, F. Guerriero, R. Musmanno,"An algorithm for solving the distributed termination detection problem":  Parallel Algorithms and Applications, Volume 14, Issue 2, pp. 149-164, (1999) Taylor and Francis, DOI 10.1080/10637199808947383, ISSN 1063-7192

[1] B. Paternoster, M. Cafaro, "Computation of interval of stability of Runge-Kutta-Nystrom methods", Journal of Symbolic Computation, Elsevier, Volume 25, Issue 3 (1998) pp. 383-394,  DOI:10.1006/jsco.1997.0183, ISSN 0747-7171

 

 

 

 

 

 

 

 

 

Book Chapters

[12] M. Cafaro, M. Pulimeno, "Frequent Itemset Mining", to appear in Business and Consumer Analytics: New Directions, Springer.

[11] M. Cafaro, I. Epicoco, M. Pulimeno, "Data Mining: Mining Frequent Patterns, Associations rules, and Correlations", to appear in Encyclopedia of Bioinformatics and Computational Biology, Elsevier.

[10] M. Cafaro, I. Epicoco, M. Pulimeno, "Techniques for designing Bioinformatics algorithms", to appear in Encyclopedia of Bioinformatics and Computational Biology, Elsevier.

[9] M. Cafaro, G. Aloisio, “Grids, Clouds and Virtualization”, in Grids, Clouds and Virtualization, Springer-Verlag, London, 2011, pp. 1-21, Series: Computer Communications and Networks. Edited by Massimo Cafaro and Giovanni Aloisio. Hardcover ISBN 978-0-85729-048-9, Softcover ISBN 978-1-4471-2592-1, eBook ISBN 978-0-85729-049-6

[8] S. Fiore, A. Negro, S. Vadacca, M. Cafaro, G. Aloisio, R. Barbera and E. Giorgio, “An Architectural Overview of the GRelC Data Access Service”, in Handbook of Research on Grid Technologies and Utility Computing: Concepts for Managing Large-Scale Applications, Emmanuel Udoh (Editor), Frank Wang (Co-Editor), pp. 98-108, IGI Global, 2009, DOI 10.4018/978-1-60566-184-1.ch010, ISBN13: 9781605661841 ISBN10: 1605661848 EISBN13: 9781605661858

[7] M. Mirto, I. Epicoco, M. Cafaro, S. Fiore, M. Passante, A. negro and G. Aloisio, “ProGenGrid: A Grid Problem Solving Environment for Bioinformatics”, in Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare, Mario Cannataro (Editor), pp. 269-291, IGI Global, 2009, DOI: 10.4018/978-1-60566-374-6.ch014, ISBN13: 9781605663746 ISBN10: 1605663743 EISBN13: 9781605663753

[6] M. Cafaro et al., “The LIBI Grid Platform for Bioinformatics”, in Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare, Mario Cannataro (Editor), pp. 577-613, IGI Global, 2009, DOI: 10.4018/978-1-60566-374-6.ch029, ISBN13: 9781605663746 ISBN10: 1605663743 EISBN13: 9781605663753

[5] S. Fiore, M. Cafaro, M. Mirto, S. Vadacca, A. Negro and G. Aloisio, "The GRelC Project: state of the art and future directions", in Advances in Parallel Computing”, “High Performance Computing and Grids in Action”, L. Grandinetti (Ed), pp. 331-344, IOS Press, 2008, ISBN 978-1-58603-839-7 (print) 978-1-60750-313-2 (online), Series “Advances in Parallel Computing”, Volume 16

[4] M. Cafaro, I. Epicoco, G. Quarta, Sandro Fiore, G. Aloisio, “Design and Implementation of a Grid Computing Environment for Remote Sensing”, in “High-Performance Computing in Remote Sensing”, A. Plaza and C. Chang  (Eds), pp. 281-308, Chapman & Hall/CRC, 2008, ISBN13 9781584886624 ISBN10 1-58488-662-5

[3] G. Aloisio, M. Cafaro, I. Epicoco, "A Grid Software Process", in "Grid Computing: Software Environments and Tools", Jose C. Cunha and Omer F. Rana (Eds), pp. 75-98, Springer-Verlag London, 2006, DOI: 10.1007/1-84628-339-6_4, Print ISBN 978-1-85233-998-2 Online ISBN 978-1-84628-339-0

[2] G. Aloisio, M. Cafaro, I. Epicoco, J. Nabrzyski, "The EU GridLab Project: A Grid Application Toolkit and Testbed", in "Engineering the Grid: Status and Perspective", pp. 123-138, L. T. Yang, Jack Dongarra, Adolfy Hoisie, Beniamino Di Martino and Hans Zima (Eds), American Scientific Publisher 2006, ISBN: 1-58883-038-1

[1] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, "The Grid Relational Catalog Project", in "Grid Computing: The New Frontiers of High Performance Computing”, Series “Advances in Parallel Computing”, Volume 14, pp.129-155, Elsevier, 2005, L. Grandinetti (Ed), ISBN: 978-0-444-51999-3

 

Proceedings of international conferences

[64] M. Pulimeno, I. Epicoco, M.  Cafaro, C. Melle and G. Aloisio, "Parallel Mining of Correlated Heavy Hitters", to appear in Proceedings of The Second International Workshop on Parallel and Distributed Data Mining (WPDM 2018), held in conjunction with the 18th International Conference on Computational Science and Its Applications (ICCSA 2018), July 2 - 5, 2018 in Melbourne, Australia

[63] M. Cafaro, I. Epicoco, G. Aloisio and M. Pulimeno, "CUDA Based Parallel Implementations of Space-Saving on a GPU," 2017 International Conference on High Performance Computing & Simulation (HPCS), Genoa, Italy, 2017, pp. 707-714. doi: 10.1109/HPCS.2017.108, ISBN 978-1-5386-3250-5

[62] M. Cafaro, M. Pulimeno, "Merging Frequent Summaries", ICTCS 2016, Proceedings of the 17th Italian Conference on Theoretical Computer Science, Lecce, Italy, September 7-9 2016, CEUR Proceedings, Volume 1720, pp. 280-285, http://ceur-ws.org/Vol-1720/

[61] M. Mirto, M. Cafaro, G. Aloisio, “Peer-to-Peer Data Discovery in Health Centers”, Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on, IEEE, pp. 343-348, 20-22 June 2013, DOI 10.1109/CBMS.2013.6627813, ISBN 978-1-4799-1053-3

[60] S. Fiore, A. Negro, S. Vadacca, M. Cafaro, G. Aloisio, R. Barbera, E. Giorgio, “Advances in the GRelC Data Access Service”, IEEE Proceedings of the International Symposium on Parallel and Distributed Processing and Applications (ISPA 2008) - December 10-12, 2008 - Sydney, Australia, IEEE, pp. 849-854, DOI:10.1109/ISPA.2008.87 ISBN 978-0-7695-3471-8

[59] M. Mirto, M. Cafaro, I. Epicoco, G. Aloisio, Advances in the ProGenGrid Workflow Management System, IEEE Proceedings of the 1st International Workshop on High Performance Data Grid (HPDataGrid'08), held in conjunction with the 9th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'08), December 1-4 2008 - Dunedin, New Zealand, IEEE, pp. 538-543, DOI:10.1109/PDCAT.2008.60, ISBN 978-0-7695-3443-5

[58] M. Mirto, S. Fiore, M. Cafaro, M. Passante and G. Aloisio, A Grid-based Bioinformatics Wrapper for Biological Databases, Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS 2008), June 17-19 2008, Jyvaskyla, Finland, IEEE, pp. 191-196, DOI:10.1109/CBMS.2008.93, ISBN 978-0-7695-3165-6

[57] S. Fiore, M. Mirto, M. Cafaro, S. Vadacca, A. Negro and G. Aloisio, A GRelC based Data Grid Management Environment, Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS 2008), June 17-19 2008, Jyvaskyla, Finland, IEEE, pp. 355-360, DOI:10.1109/CBMS.2008.125, ISBN 978-0-7695-3165-6

[56] Sandro Fiore, Massimo Cafaro, Salvatore Vadacca, Alessandro Negro, Emanuele Verdesca, Maria Mirto, Giovanni Aloisio, “Asynchronous query mechanisms within the GRelC Data Access Service”, Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks (PDCN 2008), February 12-14, 2008, Innsbruck, Austria, ACTA Press Anaheim, CA, USA, pp. 49-54, ISBN: 978-0-88986-714-7

[55] Massimo Cafaro, Daniele Lezzi, Sandro Fiore, Giovanni Aloisio, Robert Van Engelen, "The GSI plug-in for gSOAP: building cross-grid interoperable secure grid services", Proceedings of Parallel Processing and Applied Mathematics 2007, 7th International Conference, PPAM 2007, Gdansk, Poland, September 9-12, 2007, Volume 4967, pp. 894-901, Lecture Notes in Computer Science, Springer-Verlag Berlin, DOI:10.1007/978-3-540-68111-3_95, Print ISBN 978-3-540-68105-2, Online ISBN 978-3-540-68111-3

[54] Sandro Fiore, Alessandro Negro, Salvatore Vadacca, Massimo Cafaro, Maria Mirto, Giovanni Aloisio, "Advanced Grid DataBase Management with the GRelC Data Access Service", Proceedings of the 5th International Symposium on Parallel and Distributed Processing and Applications, ISPA 2007 Niagara Falls, Canada, August 29-31, 2007, Lecture Notes in Computer Science 4742, Springer-Verlag Berlin Heidelberg, pp. 683-694, DOI:10.1007/978-3-540-74742-0_61, Print ISBN 978-3-540-74741-3, Online ISBN 978-3-540-74742-0

[53] M. Cafaro, I. Epicoco, M. Mirto, D. Lezzi, G. Aloisio, "The Grid Resource Broker Workflow Engine", IEEE Proceedings of The 6th International Conference on Grid and Cooperative Computing (GCC 2006), Urumchi, Xinjiang, China, August 16-18, 2007, IEEE, pp. 725-732, DOI:10.1109/GCC.2007.120, ISBN 0-7695-2871-6

[52] S. Fiore, M. Cafaro, A. Negro, S. Vadacca, G. Aloisio, R. Barbera, E. Giorgio, "GRelC DAS: a Grid-DB Access Service for gLite Based Production Grids”, IEEE Proceedings of the Fourth International Workshop on Emerging Technologies for Next-generation GRID (ETNGRID 2007), June 18-20, 2007 - Paris (France), IEEE, in 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2007. WETICE 2007, pp. 261-266, DOI:10.1109/WETICE.2007.4407167, ISBN 978-0-7695-2879-3

[51] S. Fiore, M. Mirto, M. Cafaro, G. Aloisio, "GRelC Data Storage: a Lightweight Disk Storage Management Solution for Bioinformatics “in silico” experiments", Proceedings of the 20th IEEE International Symposium on COMPUTER-BASED MEDICAL SYSTEMS (IEEE CBMS 2007), June 20-22, 2007, Maribor (Slovenia), IEEE, pp. 495-502, DOI:10.1109/CBMS.2007.53, ISBN 0-7695-2905-4

[50] Giovanni Aloisio, Massimo Cafaro, Italo Epicoco, Sandro Fiore, Maria Mirto, "A Services Oriented System for Bioinformatics Applications on the Grid", Studies in Health Technology and Informatics, From Genes to Personalized HealthCare: Grid Solutions for the Life Sciences, Volume 126, Proceedings of HealthGrid 2007, N. Jacq et al. (Eds.), 2007, Volume 126, IOS Press, pp. 174-183, 2007, ISBN 978-1-58603-738-3

[49] Giovanni Aloisio, Massimo Cafaro, Sandro Fiore, Maria Mirto, "A Grid System for the Ingestion of Biological Data into a Relational DBMS", Proceedings of IEEE International Symposium on Bioinformatics and Life Science Computing (BLSC-07), 21-23 May 2007, Niagara Falls, Canada, in 21st International Conference on Advanced Information Networking and Applications Workshops, 2007, AINAW ’07, IEEE, pp. 713-718, DOI:10.1109/AINAW.2007.26, ISBN:978-0-7695-2847-2

[48] Giovanni Aloisio, Massimo Cafaro, Sandro Fiore, Maria Mirto, Salvatore Vadacca, “GRelC Data Gather Service: a Step Towards P2P Production Grids”, Proceedings of 22nd ACM Symposium on Applied Computing (SAC 2007), Seoul, Korea, March 11 - 15, 2007, ACM New York, NY, USA, pp. 561-565, DOI:10.1145/1244002.1244131, ISBN 1-59593-480-4

[47] G. Aloisio, M. Cafaro, I. Epicoco, S. Fiore, M. Mirto, “BioGAT: a Grid Toolkit for Bioinformatics Sequence Alignment”, Proceeding of the Grid-Enabling Legacy Applications and Supporting End Users Workshop (GELA) within the framework of the 15th IEEE International Symposium on High Performance Distributed Computing, HPDC’15, Paris, France, June 19-23, 2006, pp. 77-85, 2006

[46] G. Aloisio, M. Cafaro, S. Fiore, M. Tana, “GridSAT architecture: a step further towards security and efficiency”, Proceedings of Parallel and Distributed Computing and Networks (PDCN) – IASTED, pp. 1-6, February 14 – 16, 2006 Innsbruck, Austria, ACTA Press Anaheim, CA, USA

[45] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, "A Split&Merge Data Management Architecture for a Grid Environment", Proceedings of the 19th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS 2006), IEEE, pp. 739-744, June 22-23 2006, Salt Lake City Utah (USA), DOI:10.1109/CBMS.2006.28, ISBN 0-7695-2517-1

[44] G. Aloisio, M. Cafaro, S. Molendini, M. Tana, F. Tommasi, A. Tricco, “GridSAT: Grid enabled Satellite Architecture for Reliable Transmissions”, Proceedings of the 2nd IEEE International Symposium on Wireless Communication Systems,  IEEE, September 5-7, 2005 Siena, Italy, pp. 634-638, DOI:10.1109/ISWCS.2005.1547782, ISBN 0-7803-9206-X

[43] G. Aloisio, M. Cafaro, G. Carteni, I. Epicoco, G. Quarta, S. Raolil, "GridFlow for Earth Observation Data Processing", Proceedings of The 2005 International Conference on Grid Computing and Applications (GCA'05: June 20-23 2005, Las Vegas, USA), pp. 168-174, CSREA Press, ISBN 1-932415-57-2

[42] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, “ProGenGrid: a Grid-enabled platform for Bioinformatics”, Studies in Health Technology and Informatics, From grid to Healthgrid - Proceedings of HealthGrid 2005, Tony Solomonides et al. (Eds), IOS Press, Volume 112, IOS Press, pp. 113-126, 2005, ISBN 978-1-58603-510-5

[41] G. Aloisio, M. Cafaro, I. Epicoco, Sandro Fiore, Maria Mirto, “A Semantic Grid-based Data Access and Integration Service for Bioinformatics”, Electronic Proceedings of the 5th IEEE International Symposium on Cluster Computing and the Grid (CCGrid) 2005, IEEE, May 9-12, 2005, Cardiff, Wales, UK, pp. 196 - 203 Vol. 1 , DOI:10.1109/CCGRID.2005.1558554, ISBN:0-7803-9074-1

[40] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, “ProGenGrid: a Workflow Service Infrastructure for Composing and Executing Bioinformatics Grid Services”, Proceedings of the 18th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS 2005), IEEE, pp. 555-560, Dublin, June 23-24 2005, Ireland, DOI:10.1109/CBMS.2005.90, ISBN 0-7695-2355-2

[39] G. Aloisio, M. Cafaro, I. Epicoco, G. Quarta, “Teaching High Performance Computing Parallelizing a real Computational Science Application”, Procedings of Computational Science – ICCS 2005: 5th International Conference, Atlanta, GA, USA, May 22-25, 2005. Proceedings, Part II, Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science Volume 3515, 2005, pp 10-17, DOI:10.1007/11428848_2, Print ISBN 978-3-540-26043-1, Online ISBN 978-3-540-32114-9

[38] G. Aloisio, M. Cafaro, I. Epicoco, S. Fiore, D. Lezzi, M. Mirto, S. Mocavero, “Resource and Service Discovery in the iGrid Information Service”, Proceedings of International Conference on Computational Science and its Applications (ICCSA 2005), Singapore, May 9-12, 2005, Proceedings, Part III, Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science Volume 3482, 2005, pp 1-9, DOI:10.1007/11424857_1, Print ISBN 978-3-540-25862-9, Online ISBN 978-3-540-32045-6

[37] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto. “The Grid-DBMS: Towards Dynamic Data Management in Grid Environments”. Proceedings of IEEE International Conference on Information Technology: Coding and Computing,  ITCC 2005, 4-6 April 2005, Las Vegas, Nevada, USA, IEEE, Vol. II, pp. 199-204, 2005, DOI:10.1109/ITCC.2005.272, ISBN:0-7695-2315-3

[36] G. Aloisio, M. Cafaro, D. Conte, S. Fiore, I. Epicoco, G.P. Marra, G. Quarta, "A grid enabled Web Map Server", Proceedings of IEEE International Conference on Information Technology: Coding and Computing,  ITCC 2005, 4-6 April 2005, Las Vegas, Nevada, USA, IEEE, Volume I, pp. 298-303, 2005, DOI:10.1109/ITCC.2005.12, ISBN 0-7695-2315-3

[35] G. Aloisio, M. Cafaro, I. Epicoco, D. Lezzi, R. Van Engelen, “The GSI plug-in for gSOAP: Enhanced Security, Performance, and Reliability”, Proceedings of IEEE International Conference on Information Technology: Coding and Computing,  ITCC 2005, 4-6 April 2005, Las Vegas, Nevada, USA, IEEE, Volume I, pp. 304-309, DOI:10.1109/ITCC.2005.273, ISBN:0-7695-2315-3

[34] G. Aloisio, M. Cafaro, I. Epicoco, S. Fiore, D. Lezzi, M. Mirto and S. Mocavero, “iGrid, a Novel Grid Information Service”, Proceedings of Advances in Grid Computing - EGC 2005 (European Grid Conference, Amsterdam, The Netherlands, February 14-16, 2005, Revised Selected Papers), Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg, Volume 3470, pp. 506-515, 2005, DOI:10.1007/11508380_52, Print ISBN 978-3-540-26918-2, Online ISBN 978-3-540-32036-4

[33] G. Aloisio, M. Cafaro, S. Fiore, G. Quarta, “A Grid-Based Architecture for Earth Observation Data Access”, Proceeding of the 2005 ACM Symposium on Applied Computing (SAC 2005), ACM New York, NY, USA, March 13-17, 2005, Santa Fe, New Mexico, USA, Volume I, pp. 701-705, DOI:10.1145/1066677.1066837, ISBN 1-58133-964-0

[32] G. Aloisio, Z. Balaton, P. Boon, M. Cafaro, I. Epicoco, G. Gombás, P. Kacsuk, T. Kielmann, D. Lezzi, “Integrating Resource and Service Discovery in the CoreGrid Information Cache Mediator Component”, In Proceedings of CoreGrid Integration Workshop, 2005

[31] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, “Advanced Delivery Mechanisms in the GRelC Project”, Proceeding of 2nd International Workshop on Middleware for Grid Computing (MGC 2004), ACM New York, NY, USA, October 18 2004, Toronto, Ontario Canada, pp. 69-74, DOI:10.1145/1028493.1028505, ISBN 1-58113-950-0

[30] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, “Bioinformatics Data Access Service in the ProGenGrid System”, Proceeding of First International Workshop on Grid Computing and its Application to Data Analysis (GADA 2004), October 25-29, Larnaca, Cyprus, Greece, in On the Move to Meaningful Internet Systems 2004: OTM 2004 Workshops, Lecture Notes in Computer Science Volume 3292, 2004, pp. 211-221, Springer-Verlag Berlin Heidelberg, DOI:10.1007/978-3-540-30470-8_38, Print ISBN 978-3-540-23664-1, Online ISBN 978-3-540-30470-8

[29] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, "ProGenGrid: A Grid Framework for Bioinformatics", Proceeding of Biological and Artificial Intelligence Environments, 15th Italian Workshop on Neural Nets, WIRN VIETRI 2004 (Part 1, Pre-Wirn workshop on Computational Intelligence Methods for Bioinformatics and Bistatistics - CIBB 2004), Springer Netherlands, September 14-17 2004, Perugia, Italy, pp. 1-9, DOI:10.1007/1-4020-3432-6_1, Print ISBN 978-1-4020-3431-2, Online ISBN 978-1-4020-3432-9

[28] G. Aloisio, M. Cafaro, I. Epicoco, G. Quarta, “Information Management for Grid-Based Remote Sensing Problem Solving Environment,” Proceedings of the 2004 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'04), Las Vegas, Nevada, USA, June 21-24, 2004, Hamid R. Arabnia and Jun Ni (Ed.), pp. 887-894, Vol. II, CSREA Press, 2004, ISBN: 1-932415-24-6

[27] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, “A Gather Service in a Health Grid Environment”, Electronic proceedings of Medicon and Health Telematics 2004, IFMBE Proceedings (Springer), Volume 6, July 31 - August 05 2004, Island of Ischia, Naples, Italy

[26] G. Aloisio, M. Cafaro, R. Cesari, C. Mangia, G.P. Marra, M. Miglietta, M. Mirto, U. Rizza, I. Schipa, A. Tanzarella, “G-AQFS: Grid computing exploitation for the management of air quality in presence of complex meteorological circulations”, Proceedings of IEEE International Conference on Information Technology: Coding and Computing, April 5 to 7, 2004, Las Vegas, Nevada, IEEE, Volume II, pp. 83-87, DOI:10.1109/ITCC.2004.1286594, ISBN 0-7695-2108-8

[25] G. Aloisio, M. Cafaro, I. Epicoco, G. Quarta, “A Problem Solving Environment for Remote Sensing Data Processing”, in Proceedings of IEEE International Conference on Information Technology: Coding and Computing, April 5 to 7, 2004, Las Vegas, Nevada, IEEE, Volume II, pp. 56-61, DOI:10.1109/ITCC.2004.1286590, ISBN 0-7695-2108-8

[24] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, “The GRelC Library: A Basic Pillar in the Grid Relational Catalog Architecture”, in Proceedings of IEEE International Conference on Information Technology: Coding and Computing, April 5 to 7, 2004, Las Vegas, Nevada, IEEE Press, Volume I, pp. 372-376, DOI:10.1109/ITCC.2004.1286482, ISBN 0-7695-2108-8

[23] G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, “The GRELC Project: Towards GRID-DBMS”, Proceedings of the International Conference on Parallel and Distributed Computing and Networks (PDCN) - IASTED, pp. 1-6, Innsbruck (Austria) February 17-19, 2004, ACTA Press Anaheim, CA, USA

[22] G. Aloisio, M.C. Barba, M. Cafaro, E. Blasi, S. Fiore, M. Mirto, “Web Services for Edgebreaker Compression in a Grid Portal”, Electronic Proceedings of HealthGrid, 29-30 January, 2004, Clermont-Ferrand, Francia

[21] G. Aloisio, E. Blasi, M. Cafaro, M, S. Fiore, M. Mirto, “A virtual clinical folder on the grid”, Edited by: Callaos, N; Horimoto, K; Chen, J; et al., the 8th World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando, FL, JUL 18-21, 2004, Vol. VII pp. 113-118

[20] G. Aloisio, M. Cafaro, S. Fiore, S. Mocavero, “An adaptive scheduling infrastructure for a grid environment”, Edited by: Callaos, N; Lesso, W; Sanchez, B, the 8th World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando, FL, JUL 18-21, 2004, Vol. II pp. 11-15

[19] G. Aloisio, E. Blasi, M. Cafaro, I. Epicoco, G. Quarta, M. Tana, A. Zuccala, “A distributed architecture for remote sensing data management”, Edited by: Callaos, N; Lesso, W; Sanchez, B, the 8th World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando, FL, JUL 18-21, 2004, Vol. I pp. 236-240 

[18] G. Aloisio, M. Cafaro, I. Epicoco, D. Lezzi, M. Mirto, S. Mocavero, “The Design and Implementation of the GridLab Information Service”, Proceedings of The Second International Workshop on Grid and Cooperative Computing (GCC 2003), 7-10 December 2003, Shanghai (China), Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science Volume 3032, 2004, pp. 131-138, DOI:10.1007/978-3-540-24679-4_26, Print ISBN 978-3-540-21988-0, Online ISBN 978-3-540-24679-4

[17] G. Aloisio, E. Blasi, M. Cafaro, I. Epicoco, S. Fiore, S. Mocavero, “A Grid Environment for Diesel Engine Chamber Optimization”, Proceedings of ParCo2003, 2-5 September, Dresden (Germany), 2003, Elsevier, ISBN 0-444-51689-1, pp. 599-607

[16] G. Aloisio, M. Cafaro, D. Lezzi, R. Van Engelen, “Secure Web Services with Globus GSI and gSOAP”, Proceedings of Euro-Par 2003 Parallel Processing, 26th - 29th August 2003, Klagenfurt, Austria, Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science Volume 2790, 2003, pp .421-426, DOI:10.1007/978-3-540-45209-6_62, Print ISBN 978-3-540-40788-1, Online ISBN 978-3-540-45209-6

[15] G. Aloisio, M. Cafaro, E. Blasi, M. Mirto, S. Fiore, D. Lezzi, “Web Services for a Biomedical Imaging Portal”, Proceedings of Information Technology: Coding and Computing (ITCC 2003), IEEE, Volume IV, pp. 432-436, Las Vegas, Nevada, 28-30 April 2003, best student paper award, DOI:10.1109/ITCC.2003.1197568, ISBN 0-7695-1916-4

[14] G. Aloisio, E. Blasi, M. Cafaro, I. Epicoco, S. Fiore, M. Mirto, “Dynamic Grid Catalog Information Service”, Proceedings of the First European Across Grids Conference, February 13-14, 2003 Santiago de Compostela, Spain, Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science Volume 2970, 2004, pp 198-205, DOI:10.1007/978-3-540-24689-3_25, Print ISBN 978-3-540-21048-1, Online ISBN 978-3-540-24689-3

[13] G. Aloisio, E. Blasi, M. Cafaro, S. Fiore, M. Mirto, “A Grid Portal for Biomedical Imaging”, Edited by: Callaos, N; Lesso, W; Schewe, KD; et al., the 7th World Multiconference on Systemics, Cybernetics and Informatics, ORLANDO, FL, JUL 27-30, 2003, Vol. V pp. 57-62

[12] G. Aloisio, M. Cafaro, S. Fiore, S; I. Epicoco, M. Mirto, S. Mocavero, “A performance comparison between GRIS and LDGC Information Services”, Edited by: Callaos, N; Lesso, W; Schewe, KD; et al., the 7th World Multiconference on Systemics, Cybernetics and Informatics,  ORLANDO, FL, JUL 27-30, 2003, Vol. XII pp. 416-420 

[11] G. Aloisio, E. Blasi, M. Cafaro, M. Mirto, S. Fiore, “A genetic algorithm for medical image registration”, Edited by: Callaos, N; Whymark, G; Lesso, W, the 6th World Multi-Conference on Systemics, Cybernetics and Informatics (SCI 2002)/8th International Conference on Information Systems Analysis and Synthesis (ISAS 2002)ORLANDO, FL, JUL 14-18, 2002, pp. 192-197

[10] G. Aloisio, L. De Paolis, L. Provenzano, M. Cafaro, L. Colizzi, “Coronary stent implant simulation using haptic interface: Problems and solutions “, Edited by: Callaos, N; Whymark, G; Lesso, W, the 6th World Multi-Conference on Systemics, Cybernetics and Informatics (SCI 2002)/8th International Conference on Information Systems Analysis and Synthesis (ISAS 2002) ORLANDO, FL, JUL 14-18, 2002, pp. 570-573

[9] G. Aloisio, M. Cafaro, I. Epicoco, “SARA, a Web Based Remote Sensing Digital Library”, Electronic Proceedings of IEEE International Parallel and Distributed Processing Symposium 2002, IEEE, Fort Lauderdale, Florida, April 15-19, 2002, DOI:10.1109/IPDPS.2002.1016634

[8] G. Aloisio, M. Cafaro, E. Blasi, L. Depaolis, I. Epicoco, “The GRB Library: Grid Computing with Globus in C”, Proceedings of High-Performance Computing and Networking, the 9th International Conference HPCN Europe 2001, Amsterdam, Netherlands, June 25–27, 2001, Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science Volume 2110, 2001, pp. 133-139, DOI:10.1007/3-540-48228-8_14, Print ISBN 978-3-540-42293-8, Online ISBN 978-3-540-48228-4

[7] Allen G., Dramlitsch T., Goodale T., Lanfermann G., Radke T., Seidel E., Kielmann T., Verstoep K., Balaton Z., Kacsuk P., Szalai F., Gehring J., Keller A., Streit A., Matyska L.,  Ruda M., Krenek A., Knipp H., Merzky A., Reinefeld A., Schintke F., Ludwiczak B., Nabrzyski J., Pukacki J., Kersken H.-P., Aloisio G., Cafaro M., Ziegler W., Russell M., “Early experiences with the EGrid testbed”, Proceedings of the first IEEE/ACM symposium on Cluster Computing and the Grid, CCGRID 2001, 15-18 May 2001, Brisbane, IEEE, pp.130-137, 2001, DOI:10.1109/CCGRID.2001.923185, ISBN 0-7695-1010-8

[6] G. Aloisio, M. Cafaro, “An introduction to the Globus toolkit”, 2000 CERN School of Computing Conference,  Edited by: Vandoni, CE, MARATHON, GREECE, SEP 17- 30, 2000, Book Series: C E R N REPORTS , pp. 117-131

[5] G. Aloisio, M. Cafaro, A. Mongelli, R. Williams, “The use of Computational Grids for a Dynamic Earth Observation System”, the 4th World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando, FL, JUL 23-26, 2000, Vol. VII pp. 535-537

[4] G. Aloisio, M. Cafaro, P. Falabella, C. Kesselman, R. Williams, “Grid Computing on the Web using the Globus Toolkit”, Proceedings of High Performance Computing and Networking, the 8th International Conference HPCN Europe 2000 Amsterdam, The Netherlands, May 8–10, 2000, Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science Volume 1823, 2000, pp. 32-40, DOI:10.1007/3-540-45492-6_4, Print ISBN 978-3-540-67553-2, Online ISBN 978-3-540-45492-2

[3] M. Cafaro, B. Paternoster, “Analysis of Stability of Rational Approximations through computer algebra”, Proceedings of The Second Workshop on Computer Algebra in Scientific Computing (CASC 99), Munich, May 31-June 4, 1999, pp. 25-36, Springer-Verlag Berlin Heidelberg, DOI:10.1007/978-3-642-60218-4_2, Print ISBN 978-3-540-66047-7, Online ISBN 978-3-642-60218-4

[2] G. Aloisio, M. Cafaro, R. Williams, “The Digital Puglia Project: an Active Digital Library of remote sensing data”, Proceedings of High-Performance Computing and Networking, the 7th International Conference, HPCN Europe 1999, Amsterdam, The Netherlands, April 12–14, 1999, Springer Berlin Heidelberg, Lecture Notes in Computer Science Volume 1593, 1999, pp. 563-572, DOI:10.1007/BFb0100617, Print ISBN 978-3-540-65821-4, Online ISBN 978-3-540-48933-7

[1] G. Aloisio, M. Cafaro, R. Williams, P. Messina, “A distributed web-based metacomputing environment”, Proceedings of High-Performance Computing and Networking, the 5th International Conference, HPCN Europe 1997, Vienna, Austria, April 28–30, 1997 Springer Berlin Heidelberg, Lecture Notes in Computer Science Volume 1225, 1997, pp. 480-486, DOI:10.1007/BFb0031620, Print ISBN 978-3-540-62898-9, Online ISBN 978-3-540-69041-2

 

                                                                                                       

 

 

Temi di ricerca

Algoritmi sequenziali, paralleli e distribuiti. Cloud/grid/P2P computing. Data mining. Machine learning. Security e crittografia.