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In this chapter, the ProGenGrid (Proteomics and Genomics Grid) research project, which started in 2004, is described. It is a Grid Problem Solving Environment, specialized for the Bioinformatics domain, which aims at providing an integrated environment in order to compose, schedule and monitor biological applications in a Computational Grid. The main feature offered by this environment is the possibility to use a friendly web interface for composing workflow jobs, scheduled on different grid middleware.

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.

Grids encourage and promote the publication, sharing and integration of scientific data, distributed across Virtual Organizations. Scientists and researchers work on huge, complex and growing datasets. The complexity of data management within a grid environment comes from the distribution, heterogeneity and number of data sources. Along with coarse grained services (basically grid storages, replica services, storage resource managers, etc.), there is a strong interest on fine grained ones concerning, for instance, grid-database access and management. Moreover, as grid computing, technologies and standards evolve, more mature environments (production grids such as EGEE) become available for production based activities and tools/services able to access in grid relational databases are also strongly required. We describe the Grid Relational Catalog (GRelC) Project, a framework for grid data management, highlighting its approach, architecture, components, services and technological issues.

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.

Even though there are many useful tools for sequence alignment such as Blast by NCBI, Fasta by the University of Virginia and Smith-Waterman, by NIST, one of the key issues is that sequence databases are exploding in size, growing at an exponential rate. Grid and parallel computing are crucial techniques to maintain and improve the effectiveness of sequence comparison tools, whereas the Web Services approach may guarantee the interoperability among large collections of programs and data. This work describes BioGAT (Bioinformatics Grid Alignment Tools), a toolkit that offers optimized brokering and a data management system to exploit various bioinformatics alignment tools wrapped as Web Services in a Grid architecture.

Recently, many projects based on grid technologies have been started worldwide. The European GridLab project represents one of the most integrated projects, joining together all of the core services of a grid middleware to build a framework to develop grid-aware applications. The primary aim of GridLab is to provide users and application developers with a simple and robust environment enabling them to produce applications that can exploit the full power and possibilities of grids. The GridLab project brings together computer and computational scientists from various application areas to design and implement a grid application toolkit (GAT), together with a set of novel grid services. The project will demonstrate the benefits of the GAT by developing and im- plementing real application scenarios, illustrating wild, exciting, new uses of grids. The GAT will contain independent modules for handling various aspects of grid programming, including simulation, performance and grid monitoring, resource brokering and selection, performance prediction, interac- tion with information servers, security, notifications, collaborative working, data handling, remote visualization, and remote application steering. This chapter reports the current status of the GridLab project, highlighting the GAT and the novel grid services that have been developed.

In this paper we describe a Workflow Management System, named ProGenGrid (Proteomics and Genomics Grid, developed at the University of Lecce) which aims at providing a tool where e-scientists can simulate biological experiments through the composition of existing analysis and visualization tools, wrapped as Web Services. Since bioinformatics applications are compute- and data-intensive, needing clusters or many workstations to reduce the execution time, we exploit a Grid infrastructure for interconnecting wide-spread tools and hardware resources. As an example, we are considering some algorithms and tools needed for alignment of sequences, providing them as services, through easy to use Web interfaces and Web services built using the open source gSOAP Toolkit. As Grid middleware, we are using the Globus Toolkit 4.1, exploiting some protocols such as GSI and GridFTP.

Earth Observation techniques offer many powerful instruments for Earth planet study, urban development planning, military intelligence helping and so on. Terabytes of EO and geospatial data about lands, oceans, glaciers, cities, etc. are continuously downloaded through remote-sensing infrastructures and stored into heterogeneous, distributed repositories usually belonging to different virtual organizations. A problem-solving environment can be a viable solution to handle, coordinate and share heterogeneous and distributed resources. Moreover, grid computing is an emerging technology to solve large-scale problems in dynamic, multi-institutional Virtual Organizations coordinated by sharing resources such as high-performance computers, observation devices, data and databases over high-speed networks, etc. In this paper we present the Italian Grid for Earth Observation (I-GEO) project, a pervasive environment based on grid technology to help the integration and processing of Earth Observation data, providing a tool to share and access data, applications and computational resources among several organizations.

Aim of this work is the implementation of an integrated system for modeling air pollutants dispersion using GRID computing technologies. The sys- tem includes two meteorological models, emission pre-processors and two dispersion models for inert and photochemical pollutants. Both meteorological and dispersion models use models which run on the European scale for the initial and boundary conditions. As a test, the modeling system has been applied to the Salento Penin- sula, located in the south-east corner of Italy, in the Mediterranean central area with complex meteorological conditions.

Today many Data Grid applications need to manage and process a very large amount of data distributed across multiple grid nodes and stored in relational databases. The Grid Relational Catalog Project (GRelC) developed at the CACT/ISUFI of the University of Lecce, represents an attempt to design and deploy a Grid-DBMS for the Globus Community. In this paper, after defining the Grid-DBMS concept, we describe the GRelC library which is layered on top of the Globus Toolkit. The user can build client applications on top of it that can easily get access to and interact with data resources.

Daily, a huge quantity of Earth Observation (EO) and geospatial data is produced. These data belong to different types and are very useful in several scientific areas and applications. Storing and managing this huge quantity of data over distributed systems is a challenging problem. Moreover the association of a uniform set of metadata to the EO and geospatial data allows adding important information and searching with user's customized criteria. In this paper we present a prototype of an infrastructure of a distributed environment, aimed to store, manage and access EO and geospatial data. The described solution is also able to extract raw set of metadata from products and to convert it in a uniform set, in order to allow an unvarying description of products.

Today many Data Grid applications need to manage and process a very large amount of data distributed across multiple grid nodes. Several applications often access large databases (i.e. protein data banks, in the bioinformatics field) without any data access services taking into account characteristics of either applications or data types. Such applications could improve their performance and quality of results by using efficient, cross-DBMS, specialized and ad hoc implemented data access services. The Grid Relational Catalog Project (GRelC) developed at the CACT/ISUFI Laboratory of the University of Lecce provides a grid-enabled access service for relational and not relational repositories. In this paper we propose some advanced delivery mechanisms developed within the GRelC project, showing up experimental results related to an European testbed.

Grid workflows can produce thousands of results that should be properly organised to enable further analysis. Typically results are stored on locations hard-coded in the workflow or in the components, limiting reusability. In this paper we present an approach to (re)organise the output files generated by a grid workflow in a distributed storage environment. We propose to perform a post-mortem mapping of workflow results into a directory structure. This mapping is based on data provenance information and exploits grid catalog features, namely logical file names, to avoid data replication. By defining different mappings, users can generate their own semantic view of results generated during a workflow execution, which fosters user-friendliness, whereas preserving workflow reusability. An implementation on the Virtual Resource Browser (VBrowser) framework is detailed and evaluated on neuroimaging workflows. Results show that the complex directory structure of an image analysis application cane properly generated by our system. An initial performance evaluation of the mapping resolution and directory structure creation indicates that this approach provides a practical, simple, yet powerful solution to an important roadblock for the adoption of workflows to implement complex image analysis pipelines.