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 o more Virtual Organizations via distributed queries in a grid computing environment

The Climate-G Portal: The context, key features and a multi-dimensional analysis

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3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

Report on raising public awareness and participation (Deliverable D20)

The purpose of this document is to present actions taken during the Cyclops project lifetime in order to raise public awareness and participation, as well as the outcomes of these actions. Dissemination and outreach have always been considered key points for accomplishing this, ever since the project planning phases. The actions are generally framed in the Work Package devoted to dissemination (WP5), although some of them may well be regarded as a horizontal action of the project

High Throughput Protein Similarity Searches in the LIBI Grid Problem Solving Environment

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 LIBIPSE, is presented

Grid Database - Management, OGSA and Integration

The problem description of data models and types of databases has generated and gives rise to extensive controversy generated by their complexity, the many factors involved in the actual process of implementation. 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.Early Grid applications focused principally on the storage, replication and movement of file-based data.. Many Grid applications already use databases for managing metadata, but increasingly many are associated with large databases of domain-specific information. In this paper we will talk about the fundamental concepts related to grid-database access, management, OGSA and integration

Efficient replication of large volumes of data and maintaining data consistency by using P2P techniques in Desktop Grid

Desktop Grid is increasing in popularity because of relatively very low cost and good performance in institutions. Data-intensive applications require data management in scientific experiments conducted by researchers and scientists in Desktop Grid-based Distributed Computing Infrastructure (DCI). Some of these data-intensive applications deal with large volumes of data. Several solutions for data-intensive applications have been proposed for Desktop Grid (DG) but they are not efficient in handling large volumes of data. Data management in this environment deals with data access and integration, maintaining basic properties of databases, architecture for querying data, etc. Data in data-intensive applications has to be replicated in multiple nodes for improving data availability and reducing response time. Peer-to-Peer (P2P) is a well established technique for handling large volumes of data and is widely used on the internet. Its environment is similar to the environment of DG. The performance of existing P2P-based solution dealing with generic architecture for replicating large volumes of data is not efficient in DG-based DCI. Therefore, there is a need for a generic architecture for replicating large volumes of data efficiently by using P2P in BOINC based Desktop Grid. Present solutions for data-intensive applications mainly deal with read only data. New type of applications are emerging which deal large volumes of data and Read/Write of data. In emerging scientific experiments, some nodes of DG generate new snapshot of scientific data after regular intervals. This new snapshot of data is generated by updating some of the values of existing data fields. This updated data has to be synchronised in all DG nodes for maintaining data consistency. The performance of data management in DG can be improved by addressing efficient data replication and consistency. Therefore, there is need for algorithms which deal with data Read/Write consistency along with replication for large volumes of data in BOINC based Desktop Grid. The research is to identify efficient solutions for data replication in handling large volumes of data and maintaining Read/Write data consistency using Peer-to-Peer techniques in BOINC based Desktop Grid. This thesis presents the solutions that have been carried out to complete the research

Interoperability of heterogeneous large-scale scientific workflows and data resources

Workflow allows e-Scientists to express their experimental processes in a structured way and provides a glue to integrate remote applications. Since Grid provides an enormously large amount of data and computational resources, executing workflows on the Grid results in significant performance improvement. Several workflow management systems, which are widely used by different scientific communities, were developed for various purposes. Therefore, they differ in several aspects. This thesis outlines two major problems of existing workflow systems: workflow interoperability and data access. On the one hand, existing workflow systems are based on different technologies. Therefore, to achieve interoperability between their workflows at any level is a challenging task. In spite of the fact that there is a clear demand for interoperable workflows, for example, to enable scientists to share workflows, to leverage existing work of others, and to create multi-disciplinary workflows; currently, there are only limited, ad-hoc workflow interoperability solutions available for scientists. Existing solutions only realise workflow interoperability between a small set of workflow systems and do not consider performance issues that arise in the case of large-scale (computational and/or data intensive) scientific workflows. Scientific workflows are typically computation and/or data intensive and are executed in a distributed environment to speed up their execution time. Therefore, their performance is a key issue. Existing interoperability solutions bottleneck the communication between workflows in most scenarios dramatically increasing execution time. On the other hand, many scientific computational experiments are based on data that reside in data resources which can be of different types and vendors. Many workflow systems support access to limited subsets of such data resources preventing data level workflow interoperation between different systems. Therefore, there is a demand for a general solution that provides access to a wide range of data resources of different types and vendors. If such a solution is general, in the sense that it can be adopted by several workflow systems, then it also enables workflows of different systems to access the same data resources and therefore interoperate at data level. Note that data semantics are out of the scope of this work. For the same reasons as described above, the performance characteristics of such a solution are inevitably important. Although in terms of functionality, there are solutions which could be adopted by workflow systems for this purpose, they provide poor performance. For that reason, they did not gain wide acceptance by the scientific workflow community. Addressing these issues, a set of architectures is proposed to realise heterogeneous data access and heterogeneous workflow execution solutions. The primary goal was to investigate how such solutions can be implemented and integrated with workflow systems. The secondary aim was to analyse how such solutions can be implemented and utilised by single applications