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Reliable and timely information on the environmental conditions at sea is key to the safety of professional and recreational users as well as to the optimal execution of their activities. The possibility of users obtaining environmental information in due time and with adequate accuracy in the marine and coastal environment is defined as sea situational awareness (SSA). Without adequate information on the environmental meteorological and oceanographic conditions, users have a limited capacity to respond, which has led to loss of lives and to large environmental disasters with enormous consequent damage to the economy, society and ecosystems. Within the framework of the TESSA project, new SSA services for the Mediterranean Sea have been developed. In this paper we present SeaConditions, which is a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. Model forecasts and satellite products from operational services, such as ECMWF and CMEMS, can be visualized in SeaConditions. In addition, layers of information related to bathymetry, sea level and ocean-colour data (chl a and water transparency) are displayed. Ocean forecasts at high spatial resolutions are included in the version of SeaConditions presented here. SeaConditions provides a user-friendly experience with a fluid zoom capability, facilitating the appropriate display of data with different levels of detail. SeaConditions is a single point of access to interactive maps from different geophysical fields, providing high-quality information based on advanced oceanographic models. The SeaConditions services are available through both web and mobile applications. The web application is available at www.sea-conditions.com and is accessible and compatible with present-day browsers. Interoperability with GIS software is implemented. User feedback has been collected and taken into account in order to improve the service. The SeaConditions iOS and Android apps have been downloaded by more than 105000 users to date (May 2016), and more than 100000 users have visited the web version.

The European Network for Earth System Modelling (ENES) Climate Analytics Service (ECAS) is a new service from the EOSC-hub project. It offers a Virtual Research Environment (VRE) to scientific users, combining a Python (Jupyter) work environment with support services for data access, computing and data sharing. ECAS is motivated by providing users with remote access to extensive computing and storage resources beyond what they may have access to locally, reducing the need to conduct costly data transfer, and helping to realize the vision of FAIR data management. ECAS aims at providing a paradigm shift for the ENES community and beyond with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited or missing end-to-end analytics workflow and provenance capabilities. Furthermore, the integrated data analytics service enables basic data provenance tracking by establishing a graph of persistent identifiers (PIDs) through the whole chain, and thereby improving reusability, traceability, and reproducibility. ECAS targets multiple user groups, including researchers in lack of local computing and storage resources, researchers with interest in the high-volume climate data pools, and use within education and training scenarios.

Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe–Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness., The work shown in this article has been funded jointly by European Commission under the Cooperation Programme, Horizon2020 grant agreement No 690116 (EUBra-BIGSEA) and the Min-istériode Ciência,Tecnologiae Inovação(MCTI) from Brazil

Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro-Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP). This paper reports about some ESiWACE WP3 activities described in the deliverable D3.10 "ESiWACE Scheduler development and support activities"

Journal of grid computing 16(3), 381 - 408 (2018). doi:10.1007/s10723-018-9453-3, This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications., Published by Springer Nature, Dordrecht

The MEDiterranean Monitoring and Forecasting Center (Med-MFC) is part of the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), provided on an operational mode by Mercator Ocean in agreement with the European Commission. Specifically, Med MFC system provides regular and systematic information about the physical state of the ocean and marine ecosystems for the Mediterranean Sea. The Med-MFC service started in May 2015 from the pre-operational system developed during the MyOcean projects, consolidating the understanding of regional Mediterranean Sea dynamics, from currents to biogeochemistry to waves, interfacing with local data collection networks and guaranteeing an efficient link with other Centers in Copernicus network. The Med-MFC products include analyses, 10 days forecasts and reanalysis, describing currents, temperature, salinity, sea level and pelagic biogeochemistry. Waves products will be available in MED-MFC version in 2017. The consortium, composed of INGV (Italy), HCMR (Greece) and OGS (Italy) and coordinated by the Euro-Mediterranean Centre on Climate Change (CMCC, Italy), performs advanced R&D activities and manages the service delivery. The Med-MFC infrastructure consists of 3 Production Units (PU), for Physics, Biogechemistry and Waves, a unique Dissemination Unit (DU) and Archiving Unit (AU) and Backup Units (BU) for all principal components, guaranteeing a resilient configuration of the service and providing and efficient and robust solution for the maintenance of the service and delivery. The Med-MFC includes also an evolution plan, both in terms of research and operational activities, oriented to increase the spatial resolution of products, to start wave products dissemination, to increase temporal extent of the reanalysis products and improving ocean physical modeling for delivering new products. The scientific activities carried out in 2015 concerned some improvements in the physical, biogeochemical and wave components of the system. Regarding the currents, new grid-point EOFs have been implemented in the Med-MFC assimilation system; the climatological CMAP precipitation was replaced by the ECMWF daily precipitation; reanalysis time-series have been increased by one year. Regarding the biogeochemistry, the main scientific achievement is related to the implementation of the carbon system in the Med-MFC biogeochemistry model system already available. The new model is able to reproduce the principal spatial patterns of the carbonate system variables in the Mediterranean Sea. Further, a key result consists of the calibration of the new variables (DIC and alkalinity), which serves to the estimation of the accuracy of the new products to be released in the next version of the system (i.e. pH and pCO2 at surface). Regarding the waves, the system has been validated against in-situ and satellite observations. For example, a very good agreement between model output and in-situ observations has been obtained at offshore and/or well-exposed wave buoys in the Mediterranean Sea.

Climate and biodiversity systems are closely interlaced across a wide range of scales. To better understand the mutual interaction between climate change and biodiversity there is a strong need for multidisciplinary skills, tools and a large variety of heterogeneous, distributed data sources. In this regard, the EUBrazilCloudConnect project provides a user-centric research environment built on top of a federated cloud infrastructure across Europe and Brazil to serve scientific needs. One of the test cases implemented in this project focuses on climate change and biodiversity research. The BioClimate is the Science Gateway of the use case. It aims at providing end-users with a highly integrated environment, addressing mainly data analytics requirements. This paper presents a complete overview about BioClimate and the scientific environment delivered to the user community at the end of the project.

An efficient, secure, and interoperable data platform solution has been developed in the TESSA project to provide fast navigation and access to the data stored in the data archive, as well as a standard-based metadata management support. The platform mainly targets scientific users and the Situational Sea Awareness high-level services such as the Decision Support Systems (DSS). These datasets are accessible through the following three main components: the Data Access Service (DAS), the Metadata Service, and the Complex Data Analysis Module (CDAM). The DAS allows access to data stored into the archive by providing interfaces for different protocols. OPeNDAP, THREDDS, and WMS are just some of the solutions that have been integrated into the TESSA infrastructure. Metadata Service is the heart of the information system of the TESSA products and completes the overall infrastructure for data and metadata management. This component enables data search & discovery, and addresses interoperability by exploiting ISO standards for geospatial data (ISO 19115 and ISO 19139). Finally, the CDAM represents the back-end of the TESSA DSS by performing on-demand complex data analysis tasks.

A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).

Many e-science initiatives are currently investigating the use of cloud computing to support all kinds of scientific activities. The objective of this chapter is to describe the architecture and the deployment of the EUBrazilCC federated e-infrastructure, a Research & Development project that aims at providing a user-centric test bench enabling European and Brazilian research communities to test the deployment and execution of scientific applications on a federated intercontinental e-infrastructure. This e-infrastructure exploits existing resources that consist of virtualized data centers, supercomputers, and even opportunistically exploited desktops spread over a transatlantic geographic area. These heterogeneous resources are federated with the aid of appropriate middleware that provide the necessary features to achieve the established challenging goals. In order to elicit the requirements and validate the resulting infrastructure, three complex scientific applications have been implemented, which are also presented here.

Prevention is one of the most important stages in wildfire and other natural hazard management. Fire Danger Rating Systems (FDRSs) have been adopted by many countries to enhance wildfire prevention and suppression planning. With the aim to provide real-Time fire danger forecasts and finer-scale fire behaviour analysis, an operational fire danger prevention platform has been developed within the OFIDIA project (Operational FIre Danger preventIon plAtform). The OFIDIA Fire Danger Rating System platform consists of (1) a data archive for managing weather forecasting and wireless sensors data, (2) a data analytics platform for post-processing weather data and for computing fire danger indices, and (3) a web application system for the visualization of weather and fire index maps and related timeseries. The OFIDIA platform is also connected to a Wireless Sensor Network (WSN) that gathers data from several sites in the Apulia (Italy) and Epirus (Greece) regions. The WSN is made by a primary station and several wireless sensors dislocated in wooded areas, the data acquisition process relates to variables like air temperature, relative humidity, wind speed and direction, precipitation, solar radiation, and fuel moisture.

Climate Change research is even more becoming a data intensive and oriented scientific activity. Petabytes of climate data, big collections of datasets are continuously produced, delivered, accessed, processed by scientists and researchers at multiple sites at an international level. This work presents the Euro-Mediterranean Centre for Climate Change (CMCC) initiative, discussing data and metadata issues and dealing with both architectural and infrastructural aspects concerning the adopted grid enabled solution. A complete overview of the grid services deployed at the Centre is presented as well as the client side support (CMCC data portal and monitoring dashboard).

We present an integrated Grid system for the prediction of protein secondary structures, based on the frequent automatic update of proteins in the training set. The predictor model is based on a feed-forward multilayer perceptron (MLP) neural network which is trained with the back-propagation algorithm; the design reuses existing legacy software and exploits novel grid components. The predictor takes into account the evolutionary information found in multiple sequence alignment (MSA); the information is obtained running an optimized parallel version of the PSI-BLAST tool, based on the MPI Master–Worker paradigm. The training set contains proteins of known structure. Using Grid technologies and efficient mechanisms for running the tools and extracting the data, the time needed to train the neural network is dramatically reduced, whereas the results are comparable to a set of well-known predictor tools.

This paper describes the Grid Resource Broker (GRB), a Grid portal built leveraging a set of high-level, Globus-Toolkit-based Grid libraries called GRB libraries. The portal leverages the Liferay framework to provide users with an intuitive, highly customizable Web GUI. The underlying GRB middleware allows trusted users seamless access to their computational Grid environments. Copyright (c) 2007 John Wiley & Sons, Ltd.

Sea Situational Awareness (SSA) is strategically important for management purposes of Italian Seas and coastal areas. The lack of adequate dissemination of marine environmental data and consequent poor knowledge available for operations at sea reduce the response capacity, leading to loss of lives and potential socio-economic damages. The SSA topic is being addressed by "TESSA", an industrial research project funded under the PON "Ricerca & Competitivita 2007–2013" program of Ministero Italiano dell'Istruzione, dell'Universita' e della Ricerca. TESSA is a joint effort of research groups of operational oceanography and scientific computing, and aims to strengthen and consolidate the operational oceanography service and to integrate it with advanced technological platforms in order to disseminate information for the SSA. The first product of TESSA is “SeaConditions”, a public service providing ocean and weather forecasts for the Mediterranean Sea, on the web and mobile applications. Every day, forecasts are produced by operational services, such as the Mediterranean Monitoring and Forecasting Center (www.myocean.eu) for the ocean variables and ECMWF for the atmospheric variables. The service delivers detailed information with high spatial and temporal resolution. Main variables displayed on Google Maps are: bathymetry, weather and oceanographic forecasts and satellite ocean colour data. Ocean forecasts are given at different resolution since nested limited area models for Mediterranean sub-regions are also displayed. SeaConditions provides a user friendly interface with zoom and drag Google Maps' features allowing to display data with different levels of details. SeaConditions' main strength is to provide a single point of access to meteo-marine forecasts, which are based on advanced oceanographic models, remote sensing products and bathymetry, and to deliver high quality information. The SeaConditions products are available through web and mobile channels. The web portal www.sea-conditions.com is compatible with all modern web-browsers on all operating systems. For the mobile users, APPs were also developed to consider the different kind of screens and gesture/interactions. The APPs are available on AppleStore and Google Play.

The Ophidia project aims to provide a big data analytics platform solution that addresses scientific use cases related to large volumes of multidimensional data. In this work, the Ophidia software infrastructure is discussed in detail, presenting the entire software stack from level-0 (the Ophidia data store) to level-3 (the Ophidia web service front end). In particular, this paper presents the big data cube primitives provided by the Ophidia framework, discussing in detail the most relevant and available data cube manipulation operators. These primitives represent the proper foundations to build more complex data cube operators like the apex one presented in this paper. A massive data reduction experiment on a 1TB climate dataset is also presented to demonstrate the apex workflow in the context of the proposed framework.

Grid applications need to access, integrate, manage and process huge amount of data stored within distributed and heterogeneous databases. In order to develop high level data grid services able to federate/integrate data sources, low level grid data access services must be very modular and extensible as well as very efficient and robust. In the last few years several projects provided different grid enabled services trying to face these important challenges. One of them is the GRelC Project which provides the GRelC Data Access Service solution to access both relational and non-relational data sources in a grid environment. In this paper we highlight the asynchronous query mechanisms provided by the GRelC Data Access Service, a WS-I based and GSI/VOMS enabled data grid access service. We also report on the architectural design issues, one example of web enabled asynchronous query (HTML query), the user support, etc.

Increasingly, grid computing is becoming the paradigm of choice for building large-scale complex scientific applications. These applications are characterized as being computationally and/or data intensive, requiring computational power and storage resources well beyond the capability of a single computer. Grid environments provide distributed, geographically spread computing and storage resources made available to scientists belonging to Virtual Organizations; resource sharing is tightly controlled across multiple administrative domains through established service-level agreements. The adoption of Service-Oriented Architectures leads to grid environments characterized by grid services built using Web Services technologies that can be composed as needed to create arbitrarily complex workflows. In this context, security is a key issue that must be taken into account; another concern is interoperability among grids, a fundamental building block to develop grid-aware applications that can benefit from multiple grid environments. We present the GSI plug-in for gSOAP, an open source solution to the problem of securing Web Services in grid environments providing full interoperability between grid environments based on the Globus Toolkit and gLITE middleware.

The Ophidia project is a research effort addressing big data analytics requirements, issues, and challenges for eScience. We present here the Ophidia analytics framework, which is responsible for atomically processing, transforming and manipulating array-based data. This framework provides a common way to run on large clusters analytics tasks applied to big datasets. The paper highlights the design principles, algorithm, and most relevant implementation aspects of the Ophidia analytics framework. Some experimental results, related to a couple of data analytics operators in a real cluster environment, are also presented.

Nowadays we are facing an exponential growth of new data that is overwhelming the capabilities of companies, institutions and the society in general to manage and use it in a proper way. Ever-increasing investments in Big Data, cutting edge technologies and the latest advances in both application development and underlying storage systems can help dealing with data of such magnitude. Especially parallel and distributed approaches will enable new data management solutions that operate effectively at large scale.

The Earth System Grid Federation (ESGF) is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. The ESGF's architecture employs a system of geographically distributed peer nodes, which are independently administered yet united by the adoption of common federation protocols and application programming interfaces (APIs). The cornerstones of its interoperability are the peer-to-peer messaging that is continuously exchanged among all nodes in the federation; a shared architecture and API for search and discovery; and a security infrastructure based on industry standards (OpenID, SSL, GSI and SAML). The ESGF software is developed collaboratively across institutional boundaries and made available to the community as open source. It has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the entire model output used for the next international assessment report on climate change (IPCC-AR5) and a suite of satellite observations (obs4MIPs) and reanalysis data sets (ANA4MIPs).

Grid portals are web gateways aiming at concealing the underlying infrastructure through a pervasive, transparent, user-friendly, ubiquitous and seamless access to heterogeneous and geographically spread resources (i.e. storage, computational facilities, services, sensors, network and databases). The Climate- G Portal is the web gateway of the Climate-G testbed (an interdisciplinary research effort involving scientists both in Europe and US) and it is devoted to climate change research studies. The main goal of this paper is to present the Climate-G Portal providing a complete understanding of the international context, discussing its main requirements, challenges, architecture and key functionalities, and finally carrying out and presenting a multi-dimensional analysis of the Climate-G Portal, starting from a general schema proposed and discussed in this work.

This chapter provides a complete overview on the Grid Relational Catalog (GRelC) Project, a grid database research effort started in 2001 at the University of Salento. The project’s main features, its interoperability with gLite-based production grids, and a relevant show-case in the environmental domain are presented.

Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

Since the 1990s Grid Computing has emerged as a paradigm for accessing and managing distributed, heterogeneous and geographically spread resources, promising that we will be able to access computer power as easily as we can access the electric power grid. Later on, Cloud Computing brought the promise of providing easy and inexpensive access to remote hardware and storage resources. Exploiting pay-per-use models and virtualization for resource provisioning, cloud computing has been rapidly accepted and used by researchers, scientists and industries. In this volume, contributions from internationally recognized experts describe the latest findings on challenging topics related to grid and cloud database management. By exploring current and future developments, they provide a thorough understanding of the principles and techniques involved in these fields. The presented topics are well balanced and complementary, and they range from well-known research projects and real case studies to standards and specifications, and non-functional aspects such as security, performance and scalability. Following an initial introduction by the editors, the contributions are organized into four sections: Open Standards and Specifications, Research Efforts in Grid Database Management, Cloud Data Management, and Scientific Case Studies.With this presentation, the book serves mostly researchers and graduate students, both as an introduction to and as a technical reference for grid and cloud database management. The detailed descriptions of research prototypes dealing with spatiotemporal or genomic data will also be useful for application engineers in these fields.

Grid computing is a well-known technology to share resources such as high-performance computers, sensors, observation devices, data and databases across dynamic and multi-institutional Virtual Organizations (VOs). The complexity of data management in a grid environment comes from the distribution, scale, growing rate, heterogeneity, dynamicity of data sources. The data grid layer (grid data management system) in the overall grid computing software stack must provide a complete support in terms of access, integration, replication, monitoring, mining, management of data sources in a grid environment. The main goal of this work is to present the data access layer of the GRelC system architecture discussing in detail the vision, the main challenges, the internal architecture, the security framework and a real test case in the Earth Science and Environmental domains.

Grid computing is an emerging and enabling technology allowing organizations to easily share, integrate and manage resources in a distributed environment. Computational Grid allows running millions of jobs in parallel, but the huge amount of generated data has caused another interesting problem: the management (classification, storage, discovery etc.) of distributed data, i.e., a Data Grid specific issue. In the last decade, many efforts concerning the management of data (grid-storage services, metadata services, grid-database access and integration services, etc.) identify data management as a real challenge for the next generation petascale grid environments. This work provides an architectural overview of the GRelC DAS, a grid database access service developed in the context of the GRelC Project and currently used for production/tutorial activities both in gLite and Globus based grid environments.

With a growing trend towards grid-based data repositories and data analysis services, scientific data analysis often involves accessing multiple data sources, and analyzing the data using a variety of analysis programs. A strictly related critical challenge is the fact that data sources often hold the same type of data in a number of different formats; moreover, the formats expected and generated by various data analysis services are often distinct. In bioinformatics the data are often stored in flat files, therefore accessing them to retrieve a subset of records determined by constraints, is slower with respect to other approaches such as relational DBMS. We have developed a data grid system, built on top of specific biological data sources in flat file format, which carries out the ingestion into a relational DBMS for data integration reducing the data redundancy present in the biological flat files. In this work, we describe the prototype for the ingestion in a relational DBMS of the Swiss-2D PAGE flat file.

Grid portals are web gateways aiming at providing a pervasive and ubiquitous access in grid to computational resources, tools, instruments, datasets and metadata via standard Web protocols. Moreover, they provide enhanced problem solving capabilities to deal with modern, large scale scientific and engineering problems. Data grid management systems are becoming increasingly important in the context of the recently adopted service oriented paradigm. The grid relational catalog (GRelC) project is working towards ubiquitous, integrated, seamless and comprehensive grid database management solutions. This paper describes the GRelC Portal, a web based grid-enabled solution for grid-database access, management and integration built on top of the GRelC Data Access Service.

Nowadays production grids such as EGEE, Teragrid, DEISA adopt several tools in order to monitor jobs, check the status of the grid, manage accounting information, etc. Anyway, from the end-user perspective, monitoring the global status of the grid taking into account machines, networks, services, databases, job, etc. is not straightforward, uniform, and tightly coupled. What we present in this paper is the iGRelC dashboard, an integrated approach able to retrieve, process and display information coming from different data sources (both relational and non-relational) and published in grid by heterogeneous systems and services.

Data grid management systems are becoming increasingly important in the context of the recently adopted service oriented science paradigm. The Grid Relational Catalog (GRelC) project is working towards ubiquitous, integrated, seamless and comprehensive data grid management solutions to fully address application specific requirements. This paper describes a GRelC based environment for bioinformatics and its underlying data grid services allowing scientific users (by means of a customized grid portal) to manage data, handle, share and publish metadata, perform search and discovery activities, etc.

This chapter presents an overview of a Grid Computing Environment de- signed for remote sensing. Combining recent grid computing technologies, concepts related to problem solving environments and high performance com- puting, we show how a dynamic Earth Observation system can be designed and implemented, with the goal of management of huge quantities of data coming from space missions and for their on-demand processing and deliver- ing to final users.

Bioinformatics applications are naturally distributed, due to distribution of involved data sets, experimental data and biological databases. They require high computing power, owing to the large size of data sets and the complexity of basic computations, may access heterogeneous data, where heterogeneity is in data format, access policy, distribution, etc., and require a secure infrastructure, because they could access private data owned by different organizations. The Problem Solving Environment (PSE) is an approach and a technology that can fulfil such bioinformatics requirements. The PSE can be used for the definition and composition of complex applications, hiding programming and configuration details to the user that can concentrate only on the specific problem. Moreover, Grids can be used for building geographically distributed collaborative problem solving environments and Grid aware PSEs can search and use dispersed high performance computing, networking, and data resources. In this work, the PSE solution has been chosen as the integration platform of bioinformatics tools and data sources. In particular an experiment of multiple sequence alignment on large scale, supported by the LIBI PSE, is presented.

Data grids are middleware systems that offer secure shared storage of massive scientific datasets over wide area networks. In this paper we describe the GReIC Data Storage, a novel grid storage service which has been developed within the Grid Relational Catalog (GReIC) Project. The aim of this service is to manage efficiently, securely and transparently collections of bioinformatics data concerning "in silico" experiments on the grid promoting flexible, secure and coordinated storage resource sharing and publication across virtual organizations, taking into account current grid standards and specifications.

This paper describes the evolution of the main services of the ProGenGrid (ProteomicsGenomics Grid) system, a distributed and ubiquitous grid environment ("virtual laboratory"), based on Workflow and supporting the design, execution and monitoring of "in silico" experiments in bioinformatics.ProGenGrid is a Grid-based Problem Solving Environment that allows the composition of data sources and bioinformatics programs wrapped as Web Services (WS). The use of WS provides ease of use and fosters re-use. The resulting workflow of WS is then scheduled on the Grid, leveraging Grid-middleware services. In particular, ProGenGrid offers a modular bag of services and currently is focused on the biological simulation of two important bioinformatics problems: prediction of the secondary structure of proteins, and sequence alignment of proteins. Both services are based on an enhanced data access service.

Current production Grids involve hundreds of sites and thousands of machines. In this context, P2P solutions are well suited - with regard to existing centralized and hierarchical approaches - to implement highly scalable, decentralized, reliable and manageable grid services. In this paper we describe the GReIC Data Gather Service from an architectural and technological point of view. This service has been developed within the Grid Relational Catalog (GReIC) Project, at the Center for Advanced Computational Technologies (CACT) of the University of Lecce. The GReIC Data Gather architecture aims at integrating transparently and securely distributed and geographically spread heterogeneous grid data sources through Data Gather Service nodes connected in a P2P fashion.

This paper presents a data Grid system, built on top of specific biological data sources in flat file format, which carries out the ingestion into a relational DBMS that integrates these data. The prototype has been implemented for UniProtKB (located at EBI - European Bioinformatics Institute, UK) and UTRdb (located at ITB/CNR Bari, Italy) data banks owing to the following two reasons: a public available relational schema of the UniProtKB and UTRdb does not exist; UniProtKB is the most complete repository of proteins whereas UTRdb contains mRNA nucleotides and although the relation between nucleotides and proteins could be important for several studies, an explicit management of such relationship (cross-referenced link) is not yet available. The system also allows transparent, periodic update of both the DBMS and the involved data banks. Each component is a GSI (Grid Security Infrastructure) enabled Web Service, exploiting the gSOAP Toolkit; the system utilizes several grid nodes to carry out the data ingestion faster whilst reducing the redundance of data present into the flat files.

Currently several applications produce huge amount of data making them available for post-processing operations in order to infer new knowledge. Main issues of these applications are the need for efficient mechanisms to access data and high performance computing to obtain the results in an acceptable time. Wrapping the applications as Web services allows interoperability with others tools and in particular with grid computing environments exploiting a large set of resources through a standard interface, to support the requirements of so-called "data intensive" applications that handle large amounts of data. This paper presents the architecture of a complex data managements system leveraging the grid computing paradigm, exploiting existing middleware developed at the University of Lecce within the ProGenGrid, GReIC and GRB projects to support high throughput applications. This architecture has been specialized for a bioinformatics domain and a case study of the use of a biological application will be also described

Summary Background: In health applications, and elsewhere, 3D data sets are increasingly accessed through the Internet. To reduce the transfer time while maintaining an unaltered 3D model, adequate compression and decompression techniques are needed. Recently, Grid technologies have been integrated with Web Services technologies to provide a framework for interoperable application-to-application interaction. Objectives: The paper describes an implementation of the Edgebreaker compression technique exploiting web services technology and presents a novel approach for using such services in a Grid Portal. The Grid portal, developed at the CACT/ISUFI of the University of Lecce, allows the processing and delivery of biomedical images (CT – computerized tomography – and MRI – magnetic resonance images) in a distributed environment, using the power and security of computational Grids. Methods: The Edgebreaker Compression Web Service has been deployed on a Grid portal and allows compressing and decompressing 3D data sets using the Globus toolkit GSI (Globus Security Infrastructure) protocol. Moreover, the classical algorithm has been modified extending the compression to files containing more than one object. Results and Conclusions: An implementation of the Edgebreaker compression technique and related experimental results are presented. A novel approach for using the compression web service in a Grid portal allowing storing and preprocessing of huge 3D data sets, and subsequent efficient transmission of results for remote visualization is also described.

In this paper we describe the ProGenGrid (Proteomics and Genomics Grid) system, developed at the CACT/ISUFI of the University of Lecce which aims at providing a virtual laboratory where e-scientists can simulate biological experiments, composing existing analysis and visualization tools, monitoring their execution, storing the intermediate and final output and finally, if needed, saving the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web Services and composed through a workflow. Since bioinformatics applications need to use high performance machines or a high number of workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide-spread tools and hardware resources. As an example, we are considering some algorithms and tools needed for drug design, providing them as services, through easy to use interfaces such as the Web and Web service interfaces built using the open source gSOAP Toolkit, whereas as Grid middleware we are using the Globus Toolkit 3.2, exploiting some protocols such as GSI and GridFTP.

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.

Today many DataGrid applications need to manage and process a very large amount of data distributed across multiple grid nodes and stored into heterogeneous databases. Grids encourage and promote the publication, sharing and integration of scientifica data (distributed across several Virtual Organizations) in a more open manner than is currently the case, and many e-Science pojects have an urgent need to interconnect legacy and independently operated databases through a set os data access and integration services. The complexity of data management within a Computational Grid comes from the distribution, scale and heterogeneity of data sources. A set of dynamic and adaptive services could address specific issues related to automatic data management providing high performance and transparency as well as fully exploiting a grid infrastructure. These services should involved data migration and integration, discovery of data sources and so on, providing a transparent and dynamic layer of data virtualization. In this pape we introduce the Grid-DBMS concept, a framework for dynamic data management in a grid enviroment, highlighting its requirements, architecture, components and services. We also present an overview about the Grid Relational Catalog Project (GRelC) developed at the CACT/ISUFI of the University of Lecce, which represents a partial implementation of a Grid-DBMS for the Globus Community.

Nowadays many data grid applications need to manage and process a huge amount of data distributed across multiple grid nodes and stored into heterogeneous databases. Grids encourage the publication of scientific data in a more open manner than is currently the case, and many e-Science projects have an urgent need to interconnect legacy, and independently operated databases through a set of data access and integration services. In the data grid area a set of dynamic and adaptive services could address specific issues related to automatic data management aiming at both providing high performance and fully exploiting a grid infrastructure. In this paper we introduce the grid-DBMS concept, a framework for dynamic data management in grid environments, highlighting its requirements, architecture, components and services.