Venerdì 15 Dicembre 2017, ore 16,30-18,00, nell'Aula P1.6, il dottore di ricerca Matteo Interlandi, terrà il Seminario:
"Quick Develoment Big Data Analytics Tools"

Abstract: Implementing data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today’s DISC systems offer many special purpose programming models and Domain Specific Languages, but very little tooling for debugging and profiling programs. As a result, programmers spend countless hours in stitching together subprograms written using different APIs, and performing trial and error debugging using log traces as evidences. To aid this effort, in the last years I have help in building a set of tools allowing developers to reduce the overall time to market for their Big Data Analytics. In particular, in this talk I will describe how relational, graphs and machine learning applications can be efficiently run at scale using the tooling I contributed to develop."

Bio: Matteo Interlandi is a Research Scientist in the Cloud and Information Services Lab (CISL) at Microsoft, working on scalable Machine Learning Systems. Before Microsoft, he was a Postdoctoral Scholar in the CS Department at the University of California, Los Angeles. Prior to joining UCLA, he was Research Associate at the Qatar Computing Research Institute and at the Institute for Human and Machine Cognition. He obtained his PhD in Computer Science at the University of Modena and Reggio Emilia under the supervision of Sonia Bergamaschi.

Thursday, 07 December 2017 10:37

Seminario: Big data analytics beyond MapReduce

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Venerdì 15 Dicembre 2015, ore 14,00-16,00, nell'Aula P1.6, nell'ambito degli Insegnamenti di Big Data Analysis e Progettazione del Software del Corso di laurea magistrale in INGEGNERIA INFORMATICA, il dottore di ricerca Matteo Interlandi, terrà il  Seminario:


" Big data analytics beyond MapReduce"

Abstract: MapReduce was introduced by Google in 2004. Thanks to its functional programming abstraction and fault-tolerant distributed framework, MapReduce makes easy to write parallel programs. In 2004 MapReduce was mostly used for web indexing; however nowadays we are seeing different type of applications (relational, graphs or machine learning analytics for example) which does not fit well within the MapReduce paradigm. In this talk I will use some research projects I have been working on in the past years to (1) introduce Apache Spark and Apache REEF; (2) describe how relational, graphs and machine learning applications can be efficiently run at scale using such systems; and (3) illustrate system's pros, cons and design choices.

Bio: Matteo Interlandi is a Research Scientist in the Cloud and Information Services Lab (CISL) at Microsoft, working on scalable Machine Learning Systems. Before Microsoft, he was a Postdoctoral Scholar in the CS Department at the University of California, Los Angeles. Prior to joining UCLA, he was Research Associate at the Qatar Computing Research Institute and at the Institute for Human and Machine Cognition. He obtained his PhD in Computer Science at the University of Modena and Reggio Emilia under the supervision of Sonia Bergamaschi.

Tuesday, 05 December 2017 17:42

Inaugurazione Quix

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La Prof.ssa Sonia Bergamaschi Bergamaschi ha rappresentato Unimore, delegata dal Magnifico Rettore, all'inaugurazione della nuova sede di Modena di Quix.

Big Data: opportunità e sfide 

Prof.ssa Sonia Bergamaschi 
Università degli Studi di Modena e Reggio Emilia

Sezione di Scienze Fisiche, Matematiche e Naturali
Corso Vittorio Emanuele II, 59, Modena

Giovedì 23 novembre, ore 16:30
Sala dei Presidenti

Abstract intervento:

Big Data è il termine popolare per descrivere la crescita esponenziale, la disponibilità e l’uso di informazione sia di tipo strutturato che non strutturato. Molto si è detto sulle potenzialità dei Big Data in termine di innovazione e crescita per le imprese e la società. Quali sono le sfide tecnologiche ed etiche già vinte, quali quelle da affrontare per trarre valore dai Big data? La relazione ha l’ambizione di provare a rispondere a questo quesito.

Presso Auditorium Ferrari di Maranello.

  • INDUSTRIA 4.0, Relatore: Sonia Bergamaschi (PRESENTAZIONE)
  • ECONOMIA 4.0, Relatore: Oscar di Montigny
  • 880012993 880000126 LocandinaNuoviEr

In occasione della Giornata di formazione finalizzata allo scambio di buone pratiche e analisi delle progettualità in materia di politiche giovanili sul terriotorio regionale, organizzata il giorno mercoledì 27 settembre 2017 a Mirandola, è stato presentato il progeto "Open Linked Data".

Il progetto, iniziato nel 2015 e ancora in essere, ha avuto come obiettivo l'integrazione di sorgenti dati private e open della Pubblica Amministrazione in materia di Politiche Giovanili.

Al seguente link è accessibile la presentazione della MOMIS Dashboard e del progetto Open Linked Data per le Politiche Giovanili .

http://www.dbgroup.unimo.it/presentazioni/Incontro_Mirandola-OpenDataPoliticheGiovanili.pdf

On Tuesday, 12nd September, at the social dinner of RTSI 2017, Giovanni Simonini will receive the prize given from IEEE for the best 2016 PhD Thesis.
For more information visit http://www.computersociety.it/ieee-computer-society-italy-section-chapter-2016-phd-thesis-award/

On Wednesday, 13rd September, Giovanni Simonini will presides as a chair the session "Big Data Integration and IoT for Smart Health Care" at the IEEE RTSI 2017.

On Tuesday, 12nd September, Luca Gagliardelli and Giovanni Simonini had presented "SparkER: an Entity Resolution framework for Apache Spark" at the Young Professional event at the IEEE RTSI 2017

On Tuesday, 12 September, Sonia Bergamaschi presided as a chair the session "Services, Applications and Solutions to Challenging Problems in Smart Healthcare" at the IEEE RTSI 2017.

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