Dipartimento d'Ingegneria

AMANDA PRIN 2012 Project Featured

Written by  Friday, 11 July 2014 11:40

The Engineering Department of the University of Perugia is one of the units involved in the PRIN 2012 project "AMANDA: Algorithms for MAssive and Networked DAta". The unit is coordinated by Prof. Giuseppe Liotta, and includes the researchers of the Computer Engineering group of the Department, namely Prof. Walter Didimo, Dr. Emilio Di Giacomo, Dr. Carla Binucci, Dr. Luca Grilli, and Dr. Fabrizio Montecchiani.     

AMANDA will investigate algorithmics for massive data sets. On one hand the project will study emerging and realistic computational models and general algorithm design techniques; on the other hand it will focus on algorithmic issues specific for networked data sets. Pursuing these objectives raises hard research challenges, since the size of the data as well as their networked and evolving nature require a quantum leap in algorithmic design and engineering. These challenges are addressed in two workparts (WPs), each combining theoretical analysis with extensive experimental validation:
  • WP1 Massive Data Sets - focused on a number of methodological issues that arise when processing very large datasets and on the design of novel algorithmic solutions for specific data-intensive applications.
  • WP2 Massive and Evolving Networked Data Sets - focused on computing structural properties of massive and evolving networks and on designing ad-hoc algorithms and visual tools for supporting the mining process.
Other than the University of Perugia, the AMANDA consortium includes the Third University of Rome (general coordinator), the University of Rome "La Sapienza", the University of Rome "Tor Vergata", the University of Pisa, and the University of Padova. The goal of AMANDA is to strengthen the world leading position of Italian algorithmic research and the European excellence in science in general. Some of AMANDA's expected results are likely to be exploited by industries, thus providing them support in the big data challenge, while others have a foreseeable social impact.
Read 17142 times Last modified on Friday, 11 July 2014 14:14

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