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Cuzzocrea A.,CNR Institute for High Performance Computing and Networking
Concurrency Computation Practice and Experience | Year: 2011

Data and Knowledge Grids represent emerging and attracting application scenarios for Grid Computing, and pose novel and previously unrecognized challenges to the research community. Basically, Data and Knowledge Grids are found on high-performance Grid infrastructures, and add to the latter meaningful data- and knowledge-oriented abstractions and metaphors that perfectly marry with innovative requirements of modern complex Intelligent Information Systems. To this end, Service-oriented Architectures and Paradigms are the most popular for Grids, and on the whole represent an active and widely recognized area of Grid Computing research. In this paper, we introduce the so-called Grid-based RTSOA frameworks, which essentially combine Grid Computing with real-time service management and execution paradigms, and place emphasis for novel research perspectives in data-intensive e-science Grid applications on real-time bound constraints. Grid-based RTSOA frameworks are then specialized to the particular context of Data Transformation services over Grids, which play a relevant role for both Data and Knowledge Grids. Finally, we complete the main contribution of this paper with a rigorous theoretical model for efficiently supporting Grid-based RTSOA frameworks, with particular emphasis on the context of Data Transformation services over Grids, along with its comprehensive experimental assessment and analysis. © 2010 John Wiley & Sons, Ltd. Source


Pizzuti C.,CNR Institute for High Performance Computing and Networking | Rombo S.E.,University of Palermo
Bioinformatics | Year: 2014

Motivation: Protein-protein interaction (PPI) networks are powerful models to represent the pairwise protein interactions of the organisms. Clustering PPI networks can be useful for isolating groups of interacting proteins that participate in the same biological processes or that perform together specific biological functions. Evolutionary orthologies can be inferred this way, as well as functions and properties of yet uncharacterized proteins. Results: We present an overview of the main state-of-the-art clustering methods that have been applied to PPI networks over the past decade. We distinguish five specific categories of approaches, describe and compare their main features and then focus on one of them, i.e. population-based stochastic search. We provide an experimental evaluation, based on some validation measures widely used in the literature, of techniques in this class, that are as yet less explored than the others. In particular, we study how the capability of Genetic Algorithms (GAs) to extract clusters in PPI networks varies when different topology-based fitness functions are used, and we compare GAs with the main techniques in the other categories. The experimental campaign shows that predictions returned by GAs are often more accurate than those produced by the contestant methods. Interesting issues still remain open about possible generalizations of GAs allowing for cluster overlapping. © The Author 2014. Source


Talia D.,CNR Institute for High Performance Computing and Networking
CEUR Workshop Proceedings | Year: 2011

Cloud computing systems provide large-scale infrastructures for high-performance computing that are "elastic" since they are able to adapt to user and application needs. Clouds are used through a service-oriented interface that implements the*-as-a-service paradigm to offer Cloud services on demand. This paper discusses Cloud computing models and architectures, their use in parallel and distributed applications, and examines analogies, differences and potential synergies between Cloud computing and multi-agent systems. This analysis is lead having in mind the goal of implementing highperformance complex systems and intelligent applications by using of Cloud systems and software agents. The convergence of interests between multi-agent systems that need reliable distributed infrastructures and Cloud computing systems that need intelligent software with dynamic, flexible, and autonomous behavior can result in new systems and applications. Source


Pizzuti C.,CNR Institute for High Performance Computing and Networking
World Wide Web | Year: 2013

The detection of communities is an important problem, intensively investigated in recent years, to uncover the complex interconnections hidden in networks. In this paper a genetic based approach to discover communities in networks is proposed. The algorithm optimizes a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups. The method is efficient because the variation operators are modified to take into consideration only the actual correlations among the nodes, thus sensibly reducing the search space of possible solutions. Experiments on synthetic and real life networks show the ability of the method to successfully detect the network structure. © 2012 Springer Science+Business Media, LLC. Source


Cuzzocrea A.,CNR Institute for High Performance Computing and Networking
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

In this paper, we introduce a novel framework for estimating OLAP queries over uncertain and imprecise multidimensional data streams, along with three relevant research contributions: (i) a probabilistic data stream model, which describes both precise and imprecise multidimensional data stream readings in terms of nice confidence-interval-based Probability Distribution Functions (PDF); (ii) a possible-world semantics for uncertain and imprecise multidimensional data streams, which is based on an innovative data-driven approach that exploits "natural" features of OLAP data, such as the presence of clusters and high correlations; (iii) an innovative approach for providing theoretically-founded estimates to OLAP queries over uncertain and imprecise multidimensional data streams that exploits the well-recognized probabilistic estimators theory. © 2011 Springer-Verlag Berlin Heidelberg. Source

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