Gianvito Quarta

Globus Monitoring and Discovery Service and SensorML for Grid Sensor Networks

This paper describes an approach based on Globus toolkit for developing grid sensor networks. In fact, the globus monitoring and discovery service is used to integrate sensor networks in grid environments by means of the design of an information structure based on sensor modeling language (SensorML). A case study is also presented in order to provide some relevant details about our approach and to verify the concrete applicability of the proposed methodology

A problem solving environment for remote sensing data processing

A huge quantity of Earth observation and geospatial data is produced daily by numerous satellites launched by several worldwide space agencies. The processing of remote sensing data requires several steps, some of which are computationally intensive. The extraction of prominent information from remote sensing data requires a coordinated use of many applications and algorithms. Sharing computational resources among different scientists represents the sole way to approach the problem in order to achieve good performance. We describe a problem solving environment for remote sensing data processing layered on grid technologies. The proposed architecture allows sharing resources among different organizations taking into account each access policy defined by the owner of the resource.

A grid-based architecture for earth observation data access

A huge quantity of Earth Observation (EO) and geospatial data is daily produced by several organizations. These heterogeneous data are very useful in several scientific, civil, military and industrial applications.Securely and transparently storing, managing and accessing this huge quantity of data spread over distributed systems is a challenging problem. Grid computing offers today a way to achieve secure access to geographically spread storage and computational resources.In this paper we present the Distributed Earth Observation System Information Service (DEOSIS) a distributed information service, developed by CACT/ISUFI at the University of Lecce which aims at managing and accessing EO and geospatial heterogeneous data sources, in a grid environment.

A grid-enabled Web map server

Today Geographic Information Systems (GIS) provide several tools for studying and analyzing varied human and natural phenomena, therefore GIS and geospatial data has grown so much in both public and private organizations. A challenge is the integration of these data to get innovative and exhaustive knowledge about topics of interest. In this paper we describe the design of a Web map service (WMS) OGC-compliant, through the use of grid computing technology and demonstrate how this approach can improve, w.r.t. security, performance, efficiency and scalability, the integration of geospatial multi-source data. End users, with a single sign-on, securely and transparently, gets maps whose data are distributed on heterogeneous data sources belonging to one or more Virtual Organizations via distributed queries in a grid computing environment.

Teaching high performance computing parallelizing a real computational science application

In this paper we present our approach to teaching High Performance Computing at both the undergraduate and graduate level. For undergraduate students, we emphasize the key role of an hands on approach. Parallel computing theory at this stage is kept at minimal level since this knowledge is fundamental, but our main goal for undergraduate students is the required ability to develop real parallel applications. For this reason we spend about one third of the class lectures on the theory and remaining two thirds on programming environments, tools and libraries for development of parallel applications. The availability of widely adopted standards provides us, as teachers of high performance computing, with the opportunity to present parallel algorithms uniformly, to teach how portable parallel software must be developed, how to use parallel libraries etc. When teaching at the graduate level instead, we spend more time on theory, highlighting all of the relevant aspects of parallel computation, models, parallel complexity classes, architectures, message passing and shared memory paradigms etc. In particular, we stress the key points of design and analysis of parallel applications. As a case study, we present to our students the parallelization of a real computational science application, namely a remote sensing SAR (Synthetic Aperture Radar) processor, using both MPI and OpenMP.

NDVI fluctuations from 1995 to 2006 in South Italy and North Africa: a search for a climate change indicator

Seasonal and interannual vegetation trends in the last eleven years were analyzed for two macro-regions, South Italy and North Africa, in search for evidence of climate changes and associated desertification processes. The South Italy macro-region comprises Apulia, Campania, Basilicata, Calabria and Sicily regions, while the North Africa one covers the northern part of Lybia. Vegetation index data for the whole Europe and North Africa can be retrieved from the DLR archive of thematic maps in the form of monthly composite Normalized Difference Vegetation Index (NDVI) maps. The DLR archive dates back to 1995, thus the analysis could only be carried out for the last eleven years. The analysis of temporal vegetation variations was performed by implementing specific routines which provide objective measurements of vegetation trends and anomaly. Rainfall data for the same periods and geographic areas, were also analyzed in order to investigate the correlation between the two phenomena. Results for the two selected macro-regions, from 1995 to 2006, are presented and discussed. In a successive phase, this study will focus on distinguishing vegetation variations at regional level, in order to compare different local trends.