Geospatial Cyberinfrastructure and Geoprocessing Web — A Review of Commonalities and Differences of E-Science Approaches
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paper_title: Geospatial Cyberinfrastructure : Past , present and future

paper_content:

A Cyberinfrastructure (CI) is a combination of data resources, network protocols, computing platforms, and computational services that brings people, information, and computational tools together to perform science or other data-rich applications in this information-driven world. Most science domains adopt intrinsic geospatial principles (such as spatial constraints in phenomena evolution) for large amounts of geospatial data processing (such as geospatial analysis, feature relationship calculations, geospatial modeling, geovisualization, and geospatial decision support). Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains (such as the environmental and Earth sciences). GCI is based on recent advancements in geographic information science, information technology, computer networks, sensor networks, Web computing, CI, and e-research/e-science. This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Similar to how GIS transformed the procedures for geospatial sciences, GCI provides significant improvements to how the sciences that need geospatial information will advance. The evolution of GCI will produce platforms for geospatial science domains and communities to better conduct research and development and to better collect data, access data, analyze data, model and simulate phenomena, visualize data and information, and produce knowledge. To achieve these transformative objectives, col
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paper_title: What is cyberinfrastructure

paper_content:

Cyberinfrastructure is a word commonly used but lacking a single, precise definition. One recognizes intuitively the analogy with infrastructure, and the use of cyber to refer to thinking or computing -- but what exactly is cyberinfrastructure as opposed to information technology infrastructure? Indiana University has developed one of the more widely cited definitions of cyberinfrastructure. Cyberinfrastructure consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible. A second definition, more inclusive of scholarship generally and educational activities, has also been published and is useful in describing cyberinfrastructure: Cyberinfrastructure consists of computational systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible. In this paper, we describe the origin of the term cyberinfrastructure based on the history of the root word infrastructure, discuss several terms related to cyberinfrastructure, and provide several examples of cyberinfrastructure
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paper_title: Digital Earth 2020: towards the vision for the next decade

paper_content:

Abstract This position paper is the outcome of a brainstorming workshop organised by the International Society for Digital Earth (ISDE) in Beijing in March 2011. It argues that the vision of Digital Earth (DE) put forward by Vice-President Al Gore 13 years ago needs to be re-evaluated in the light of the many developments in the fields of information technology, data infrastructures and earth observation that have taken place since. The paper identifies the main policy, scientific and societal drivers for the development of DE and illustrates the multi-faceted nature of a new vision of DE grounding it with a few examples of potential applications. Because no single organisation can on its own develop all the aspects of DE, it is essential to develop a series of collaborations at the global level to turn the vision outlined in this paper into reality.
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paper_title: Creating web service interfaces and scientific workflows using command line tools: A GRASS example

paper_content:

With tremendous increase in data from earth and space based sensors, there is growing realization within geoscience community that technologies like web services and allied technologies need to be leveraged for geospatial data analysis and management. Traditionally, standalone Geographic Information Systems software was used for these tasks. Adaptation of this software for use in web services raises several issues and challenges. We have created large number of web services on top of Geographic Resources Analysis Support System (GRASS GIS), which is an open source command line GIS software. In this paper the conceptual architecture used in developing the web services is presented in detail. This architecture has different layers of services. The user of services at each layer is immune from having to know the details of lower level services and software. The geospatial model developer, who develops services at highest layer, can work at conceptual level without having to learn specifics of any particular software. The model developer can concentrate on the model itself rather than on many unnecessary and time consuming details. Some of the issues that we encountered during the development — granularity of services, statelessness of services and interoperability between services - are also discussed.
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paper_title: Cyberinfrastructure for e-Science

paper_content:

Here we describe the requirements of an e-Infrastructure to enable faster, better, and different scientific research capabilities. We use two application exemplars taken from the United Kingdom’s e-Science Programme to illustrate these requirements and make the case for a service-oriented infrastructure.We provide a brief overview of the UK ‘‘plug-andplay composable services’’ vision and the role of semantics in such an e-Infrastructure.
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paper_title: eHabitat : a Contribution to the Model Web for Habitat Assessments and Ecological Forecasting

paper_content:

In striving to improve the predictive capabilities of ecological forecasting we face three basic choices: develop new models, improve existing ones or increase the connectivity of models so they can work together. The latter approach of chaining different interoperable models is of particular interest, as technical developments have made it increasingly viable to combine models that can answer more questions than the individual models alone, allowing users to address complex questions, often of a multi-disciplinary nature. This concept of a Model Web encourages the setting up of a dynamic network of interoperating models, communicating with each other using standardized web services. It is the purpose of this paper to introduce the potential contribution of eHabitat to the Model Web. eHabitat is conceived as a Web Processing Service for computing the likelihood of finding ecosystems with equal properties. By developing eHabitat according to Model Web principles, end-users can define the thematic layers for input to the model from various sources. These input layers are discovered using standards-based catalogues, which are a fundamental component of Model Web and generic Spatial Data Infrastructures. eHabitat integrates data ranging from remote sensing data to socio-economical indicators, thus offering a huge potential for multi-disciplinary modelling.
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paper_title: GeoPW : Laying Blocks for the Geospatial Processing Web 1

paper_content:

Recent advances in Web-related technologies have significantly promoted the wide sharing and integrated analysis of distributed geospatial data. Geospatial applications often involve diverse sources of data and complex geoprocessing functions. Existing Web-based GIS focuses more on access to distributed geospatial data. In scientific problem solving, the ability to carry out geospatial analysis is essential to geoscientific discovery. This article presents the design and implementation of
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paper_title: Geospatial Cyberinfrastructure : Past , present and future

paper_content:

A Cyberinfrastructure (CI) is a combination of data resources, network protocols, computing platforms, and computational services that brings people, information, and computational tools together to perform science or other data-rich applications in this information-driven world. Most science domains adopt intrinsic geospatial principles (such as spatial constraints in phenomena evolution) for large amounts of geospatial data processing (such as geospatial analysis, feature relationship calculations, geospatial modeling, geovisualization, and geospatial decision support). Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains (such as the environmental and Earth sciences). GCI is based on recent advancements in geographic information science, information technology, computer networks, sensor networks, Web computing, CI, and e-research/e-science. This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Similar to how GIS transformed the procedures for geospatial sciences, GCI provides significant improvements to how the sciences that need geospatial information will advance. The evolution of GCI will produce platforms for geospatial science domains and communities to better conduct research and development and to better collect data, access data, analyze data, model and simulate phenomena, visualize data and information, and produce knowledge. To achieve these transformative objectives, col
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paper_title: Developing a grid-enabled spatial Web portal for Internet GIServices and geospatial cyberinfrastructure

paper_content:

Geospatial cyberinfrastructure integrates distributed geographic information processing (DGIP) technology, high-performance computing resources, interoperable Web services, and sharable geographic knowledge to facilitate the advancement of geographic information science (GIScience) research, geospatial technology, and geographic education. This article addresses three major development issues of geospatial cyberinfrastructure: the performance of grid-enabled DGIP services, the integration of Internet GIService resources, and the technical challenges of spatial Web portal implementation. A four-tier grid-enabled Internet GIService framework was designed for geospatial cyberinfrastructure. The advantages of the grid-enabled framework were demonstrated by a spatial Web portal. The spatial Web portal was implemented based on current available Internet technologies and utilizes multiple computing resources and high-performance systems, including local PC clusters and the TeraGrid. By comparing their performance testing results, we found that grid computing (TeraGrid) is more powerful and flexible than local PC clusters. However, job queuing time and relatively poor performance of cross-site computation are the major obstacles of grid computing for geospatial cyberinfrastructure. Detailed analysis of different computational settings and performance testing contributes to a deeper understanding of the improvements of DGIP services and geospatial cyberinfrastructure. This research demonstrates that resource/service integration and performance improvement can be accomplished by deploying the new four-tier grid-enabled Internet GIService framework. This article also identifies four research priorities for developing geospatial cyberinfrastructure: the design of GIS middleware, high-performance geovisualization methods, semantic GIService, and the integration of multiple GIS grid applications.
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paper_title: A GIS-based management and publication framework for data handling of numerical model results

paper_content:

There has been an increasing awareness that large-scale datasets of earth-system model results would be produced and accumulated rapidly within the next few decades with extensive usage of numerical models. The successful exploration of these data for scientific research and engineering applications requires the capability of efficient storage, retrieval, integration and visualization of these large datasets. Robust publication methods are also needed to enable data exchange through the web to increase global research collaborations. In this study, a comprehensive GIS-based data management and publication framework (GMPF) was developed and applied to the use, representation, and analysis of hydrodynamic modeling results of Hamburg Port, Germany. Linking with GIS, a desktop- and a web-based model-results data atlas were developed to provide an interactive environment for various cartography and time series plots. The implementation of the framework was based on modular software design, open specifications and reuse of open source projects. The study results showed that the project life-cycle was shortened and development efforts were reduced through applying GMPF. The developed system could be integrated into commercial hydrodynamics software platforms to enhance the capability of model-results management and publication.
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paper_title: The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society

paper_content:

This volume emphasizes the applications and implications of the Geospatial Web and the role of contextual knowledge in shaping the emerging network society. There is a clear focus on applied geospatial aspects. The book has contributions from a very active research community. Containing chapters from renowned researchers and practitioners, this volume will be invaluable to all interested in this field.
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paper_title: Toward the semantic geospatial web

paper_content:

With the growth of the World Wide Web has come the insight that currently available methods for finding and using information on the web are often insufficient. In order to move the Web from a data repository to an information resource, a totally new way of organizing information is needed. The advent of the Semantic Web promises better retrieval methods by incorporating the data's semantics and exploiting the semantics during the search process. Such a development needs special attention from the geospatial perspective so that the particularities of geospatial meaning are captured appropriately. The creation the Semantic Geospatial Web needs the development multiple spatial and terminological ontologies, each with a formal semantics; the representation of those semantics such that they are available both to machines for processing and to people for understanding; and the processing of geospatial queries against these ontologies and the evaluation of the retrieval results based on the match between the semantics of the expressed information need and the available semantics of the information resources and search systems. This will lead to a new framework for geospatial information retrieval based on the semantics of spatial and terminological ontologies. By explicitly representing the role of semantics in different components of the information retrieval process (people, interfaces, search systems, and information resources), the Semantic Geospatial Web will enable users to retrieve more precisely the data they need, based on the semantics associated with these data.
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paper_title: Building an intelligent geospatial cyberinfrastructure: an analytical problem solving approach

paper_content:

Establishing a geospatial cyberinfrastructure is essential for the future development of geospatial information systems and services. Geospatial cyberinfrastructure will foster seamless integration of heterogeneous geospatial information, web-based mapping and geo-analytical services across the Internet. This paper will first discuss the significance of geospatial cyberinfrastructure for the GIS community and highlight several underpinning techniques of geospatial cyberinfrastructure, including Grid computing, Web Services, OGC (Open Geospatial Consortium) interoperability standards and Semantic Web. Emphasis is given to the discussion of developing intelligent and analytical geospatial information services in the geospatial cyberinfrastructure. An analytical problem solving multi-tier architecture is proposed as a theoretical foundation for the geospatial cyberinfrastructure. Part of the architecture is demonstrated by a simplified prototype which is used to test the feasibility of the proposed multi-tier geospatial cyberinfrastructure.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: A Scientific Workflow Approach to Distributed Geospatial Data Processing using Web Services

paper_content:

Geospatial analyses of distributed data from surveys and sensors are often stored and managed in diverse regional, national and global repositories. The nature of scientific processes requires composition of these resources in a meaningful order to solve a specific geoscience problem. These tasks can be viewed as scientific workflows. Web based interfaces allow access to remote data and tools, and enable running computational experiments using different online resources. However, it requires manual processing to combine multiple resources in pipelines and scientists still need IT experts to automate their large-scale scientific workflows. The challenging problem is how to enable the scientists to harvest online data and models for designing and executing experiments in a seamless manner. A solution becomes feasible by the introduction of Web Services in a variety of scientific domains. These services can be discovered and composed through generic visual interfaces and scientific workflow tools. For this purpose, we present a complete framework for registering, discovering, composing and executing Web Services to support online science.
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paper_title: Designing Service Architectures for Distributed Geoprocessing: Challenges and Future Directions

paper_content:

In this paper we study the feasibility of using services offered by a Spatial Data Infrastructure as a basis for distributed service oriented geoprocessing. By developing a prototype we demonstrate that a Spatial Data Infrastructure facilitates rapid development of applications that solve typical problems for an existing risk management application. The prototype provides users with a distributed application that enables the assessment of fire damage areas based on land cover data in a given area. The services involved in the application include: Web Feature Services, Web Map Services, a Gazetteer Service, a Catalogue Service, and Geoprocessing Services. We present the architecture of the application and describe details about implementation-specific issues. We conclude that current OGC specifications provide a sound basis for developing service oriented architectures for geographic applications; however, in particular for geoprocessing applications, we question the
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paper_title: Which Service Interfaces fit the Model Web

paper_content:

The Model Web has been proposed as a concept for integrating scientific models in an interoperable and collaborative manner. However, four years after the initial idea was formulated, there is still no stable long term solution. Multiple authors propose Web Service based approaches to model publication and chaining, but current implementations are highly case specific and lack flexibility. This paper discusses the Web Service interfaces, which are required for supporting integrated environmental modeling in a sustainable manner. We explore ways to expose environmental models and their components using Web Service interfaces. Our discussions present work in progress for establishing the Web Services technological grounds for simplifying information publication and exchange within the Model Web. As a main outcome, this contribution identifies challenges in respect to the required geo- processing and relates them to currently available Web Service standards.
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paper_title: Towards Spatial Data Infrastructures in the Clouds

paper_content:

Cloud Computing is one of the latest hypes in the mainstream IT world. In this context, Spatial Data Infrastructures (SDIs) have not been considered yet. This paper reviews this novel technology and identifies the paradigm behind it with regard to SDIs. Concepts of SDIs are analyzed in respect to common gaps which can be solved by Cloud Computing technologies. A real world use case will be presented, which benefits largely from Cloud Computing as a proof-of-concept demonstration. This use case shows that SDI components can be integrated into the cloud as value-added services. Thereby SDI components are shifted from a Software as a Service cloud layer to the Platform as a Service cloud layer, which can be regarded as a future direction for SDIs to enable geospatial cloud interoperability.
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paper_title: Scientific geodata infrastructures: challenges, approaches and directions

paper_content:

Based on various experiences in developing Geodata Infrastructures (GDIs) for scientific applications, this article proposes the concept of a Scientific GDI that can be used by scientists in environmental and earth sciences to share and disseminate their research results and related analysis methods. Scientific GDI is understood as an approach to tackle the science case in Digital Earth and to further enhance e-science for environmental research. Creating Scientific GDI to support the research community in efficiently exchanging data and methods related to the various scientific disciplines forming the basis of environmental studies poses numerous challenges on today's GDI developments. The paper summarizes requirements and recommendations on the publication of scientific geospatial data and on functionalities to be provided in Scientific GDI. Best practices and open issues for governance and policies of a Scientific GDI are discussed and are concluded by deriving a research agenda for the next decade.
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paper_title: Business logic for geoprocessing of distributed geodata

paper_content:

This paper describes the development of a business-logic component for the geoprocessing of distributed geodata. The business logic acts as a mediator between the data and the user, therefore playing a central role in any spatial information system. The component is used in service-oriented architectures to foster the reuse of existing geodata inventories. Based on a geoscientific case study of groundwater vulnerability assessment and mapping, the demands for such architectures are identified with special regard to software engineering tasks. Methods are derived from the field of applied Geosciences (Hydrogeology), Geoinformatics, and Software Engineering. In addition to the development of a business logic component, a forthcoming Open Geospatial Consortium (OGC) specification is introduced: the OGC Web Processing Service (WPS) specification. A sample application is introduced to demonstrate the potential of WPS for future information systems. The sample application Geoservice Groundwater Vulnerability is described in detail to provide insight into the business logic component, and demonstrate how information can be generated out of distributed geodata. This has the potential to significantly accelerate the assessment and mapping of groundwater vulnerability. The presented concept is easily transferable to other geoscientific use cases dealing with distributed data inventories. Potential application fields include web-based geoinformation systems operating on distributed data (e.g. environmental planning systems, cadastral information systems, and others).
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paper_title: Towards a research agenda for geoprocessing services

paper_content:

Geoprocessing services are currently a hot topic in research and industry for enabling the dynamic generation of web-based geoinformation in Spatial Data Infrastructures. An overview and an in-depth analysis of this new approach are missing yet. However, they are necessary to support upcoming projects and to streamline research towards fully web-based geoinformation. Thus, this paper provides a detailed overview about recent publications and projects concerning geoprocessing services. Research commonalities among these works are extracted. Based on this analysis, a research agenda for geoprocessing services is formulated and discussed. The research agenda identifies three major research issues, which are promising for enabling web-based geoinformation: Semantic descriptions of geoprocessing services, orchestration of geoprocessing services and approaches for performance enhancements.
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paper_title: Geospatial Cyberinfrastructure : Past , present and future

paper_content:

A Cyberinfrastructure (CI) is a combination of data resources, network protocols, computing platforms, and computational services that brings people, information, and computational tools together to perform science or other data-rich applications in this information-driven world. Most science domains adopt intrinsic geospatial principles (such as spatial constraints in phenomena evolution) for large amounts of geospatial data processing (such as geospatial analysis, feature relationship calculations, geospatial modeling, geovisualization, and geospatial decision support). Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains (such as the environmental and Earth sciences). GCI is based on recent advancements in geographic information science, information technology, computer networks, sensor networks, Web computing, CI, and e-research/e-science. This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Similar to how GIS transformed the procedures for geospatial sciences, GCI provides significant improvements to how the sciences that need geospatial information will advance. The evolution of GCI will produce platforms for geospatial science domains and communities to better conduct research and development and to better collect data, access data, analyze data, model and simulate phenomena, visualize data and information, and produce knowledge. To achieve these transformative objectives, col
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paper_title: A Vision for Cyberinfrastructure for Coastal Forecasting and Change Analysis

paper_content:

This paper gives an overview of a recently initiated cyberinfrastructure project at The Ohio State University. This project proposes to develop and evaluate a cyberinfrastructure component for environmental applications. This will include developments in middleware, model integration, analysis, and mining techniques, and the use of a service model for supporting two closely related applications. These applications will be real-time coastal nowcasting and forecasting, and long-term coastal erosion analysis and prediction.
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paper_title: What is cyberinfrastructure

paper_content:

Cyberinfrastructure is a word commonly used but lacking a single, precise definition. One recognizes intuitively the analogy with infrastructure, and the use of cyber to refer to thinking or computing -- but what exactly is cyberinfrastructure as opposed to information technology infrastructure? Indiana University has developed one of the more widely cited definitions of cyberinfrastructure. Cyberinfrastructure consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible. A second definition, more inclusive of scholarship generally and educational activities, has also been published and is useful in describing cyberinfrastructure: Cyberinfrastructure consists of computational systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible. In this paper, we describe the origin of the term cyberinfrastructure based on the history of the root word infrastructure, discuss several terms related to cyberinfrastructure, and provide several examples of cyberinfrastructure
---
paper_title: Towards Spatial Data Infrastructures in the Clouds

paper_content:

Cloud Computing is one of the latest hypes in the mainstream IT world. In this context, Spatial Data Infrastructures (SDIs) have not been considered yet. This paper reviews this novel technology and identifies the paradigm behind it with regard to SDIs. Concepts of SDIs are analyzed in respect to common gaps which can be solved by Cloud Computing technologies. A real world use case will be presented, which benefits largely from Cloud Computing as a proof-of-concept demonstration. This use case shows that SDI components can be integrated into the cloud as value-added services. Thereby SDI components are shifted from a Software as a Service cloud layer to the Platform as a Service cloud layer, which can be regarded as a future direction for SDIs to enable geospatial cloud interoperability.
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paper_title: Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

paper_content:

Abstract The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential eleme...
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paper_title: NCSA environmental cyberinfrastructure demonstration project: creating cyberenvironments for environmental engineering and hydrological science communities

paper_content:

NCSA's Environmental Cyberinfrastructure Demonstration (ECID) project is developing a cyberenvironment to support environmental science and hydrology research. The ECID Cyberenvironment is based on a set of coordinating technologies that together provide unique capabilities for integrating local and remote work and for capturing and exploiting data and interaction provenance. The CyberCollaboratory portal provides collaboration tools to discover, share, analyze, and discuss data and information. The CyberIntegrator is a meta-workflow engine combining heterogeneous (local and remote) tools into workflows to support complex scientific analyses and simulations. Both of these tools record rich provenance into the RDF-based Tupelo 2 Toolkit where it can be graphically browsed or used through CI-KNOW, social network analysis software, to provide context-specific recommendations within the CyberCollaboratory and CyberIntegrator. These capabilities, and the ability to dynamically connect new tools into group spaces, workflows, and provenance trails, will be critical to the next-generation of community-scale, persistent cyberinfrastructure efforts.
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paper_title: The GRID: Blueprint for a New Computing Infrastructure

paper_content:

Edited by Tianruo Yang Kluwer Academic Publisher, Dordrech, Netherlands, 1999, 248 pp. ISBN 0-7923-8588-8, $135.00 This book contains a selection of contributed and invited papers presented and the workshop Frontiers of Parallel Numerical Computations and Applications, in the IEEE 7th Symposium on the Frontiers on Massively Parallel Computers (Frontiers '99) at Annapolis, Maryland, February 20-25, 1999. Its main purpose is to update the designers and users of parallel numerical algorithms with the latest research in the field. A broad spectrum of topics on parallel numerical computations, with applications to some of the more challenging engineering problems, is covered. Parallel algorithm designers and engineers who use extensively parallel numerical computations, as well as graduate students in Computer Science, Scientific Computing, various engineering fields and applied mathematics should benefit from reading it. The first part is addressed to a larger audience and presents papers on parallel numerical algorithms. Two new libraries are presented: PSPASSES and PoLAPACK. PSPASSES is a collection of parallel direct solvers, for sparse symmetric positive definite linear systems, which are characterized by high performance and good scalability. PoLAPACK library contains LU and QR codes based on a new blocking strategy that guarantees good performance regardless of the physical block size. Next, an efficient approach to solving stiff ordinary differential equations by diagonal implicitly iterated Runge-Kutta (DIIRK) method is described. DIIRK renders a fast parallel implementation due to a reduced number of function evaluation and an automatic stepsize control mechanism. Finally, minimization of sufficiently smooth non-linear functionals is sought via parallel space decomposition. Here, a theoretical background of the problem and two equivalent algorithms are presented. New research directions for classical solvers are treated in the next three papers: first, reduction of the global synchronization in the biconjugate gradient method, second, a new more efficient Jacobi ordering for the multiple-port hypercubes, and finally, an analysis of the theoretical performance of an improved version of the Quasi-minimal residual method. Parallel numerical applications constitute the second part of the book, with results from fluid mechanics, material sciences, applications to signal and image processing, dynamic systems, semiconductor technology and electronic circuits and systems design. With one exception, the authors expose in detail parallel implementations of the algorithms and numerical results. First, a 3D-elasticity problem is solved using an additive overlapping domain decomposition algorithm. Second, an overlapping mesh technique is used in a parallel solver for the compressible flow problem. Then, a parallel version of a complex numerical algorithm to solve a lubrication problem studied in tribology is introduced. Next, a timid approach to parallel computing of the cavity flow by the finite element method is presented. The problem solved is rather small for today's needs and only up to 6 processors are used. This is also the only paper that does not present results from numerical experiments. The remaining applications discussed in the subsequent chapters are: large scale multidisciplinary design optimization problem with application to the design of a supersonic commercial aircraft, a report on progress in parallel solution of an electromagnetic scattering problem using boundary integral methods and an optimal solution to the convection-diffusion equation modeling the concentration of a pollutant in the air. The book is of definite interest to readers who keep up-to-date with the parallel numerical computation research. The main purpose, to present the novel ideas, results and work in progress and advancing state-of-the-art techniques in the area of parallel and distributed computing for numerical and computational optimization problems in scientific and engineering application is clearly achieved. However, due to its content it cannot serve as a textbook for a computer science or engineering class. Overall, is a reference type book to be kept by specialists and in a library rather than a book to be purchased for self-introduction to the field. Most of the papers presented are results of ongoing research and so they rely heavily on previous results. On the other hand, with only one exception, the results presented in the papers are a great source of information for the researchers currently involved in the field. Michelle Pal, Los Alamos National Laboratory
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paper_title: GeoPW : Laying Blocks for the Geospatial Processing Web 1

paper_content:

Recent advances in Web-related technologies have significantly promoted the wide sharing and integrated analysis of distributed geospatial data. Geospatial applications often involve diverse sources of data and complex geoprocessing functions. Existing Web-based GIS focuses more on access to distributed geospatial data. In scientific problem solving, the ability to carry out geospatial analysis is essential to geoscientific discovery. This article presents the design and implementation of
---
paper_title: GeoPW : Laying Blocks for the Geospatial Processing Web 1

paper_content:

Recent advances in Web-related technologies have significantly promoted the wide sharing and integrated analysis of distributed geospatial data. Geospatial applications often involve diverse sources of data and complex geoprocessing functions. Existing Web-based GIS focuses more on access to distributed geospatial data. In scientific problem solving, the ability to carry out geospatial analysis is essential to geoscientific discovery. This article presents the design and implementation of
---
paper_title: GeoPW : Laying Blocks for the Geospatial Processing Web 1

paper_content:

Recent advances in Web-related technologies have significantly promoted the wide sharing and integrated analysis of distributed geospatial data. Geospatial applications often involve diverse sources of data and complex geoprocessing functions. Existing Web-based GIS focuses more on access to distributed geospatial data. In scientific problem solving, the ability to carry out geospatial analysis is essential to geoscientific discovery. This article presents the design and implementation of
---
paper_title: Toward the semantic geospatial web

paper_content:

With the growth of the World Wide Web has come the insight that currently available methods for finding and using information on the web are often insufficient. In order to move the Web from a data repository to an information resource, a totally new way of organizing information is needed. The advent of the Semantic Web promises better retrieval methods by incorporating the data's semantics and exploiting the semantics during the search process. Such a development needs special attention from the geospatial perspective so that the particularities of geospatial meaning are captured appropriately. The creation the Semantic Geospatial Web needs the development multiple spatial and terminological ontologies, each with a formal semantics; the representation of those semantics such that they are available both to machines for processing and to people for understanding; and the processing of geospatial queries against these ontologies and the evaluation of the retrieval results based on the match between the semantics of the expressed information need and the available semantics of the information resources and search systems. This will lead to a new framework for geospatial information retrieval based on the semantics of spatial and terminological ontologies. By explicitly representing the role of semantics in different components of the information retrieval process (people, interfaces, search systems, and information resources), the Semantic Geospatial Web will enable users to retrieve more precisely the data they need, based on the semantics associated with these data.
---
paper_title: Cyberinfrastructure for e-Science

paper_content:

Here we describe the requirements of an e-Infrastructure to enable faster, better, and different scientific research capabilities. We use two application exemplars taken from the United Kingdom’s e-Science Programme to illustrate these requirements and make the case for a service-oriented infrastructure.We provide a brief overview of the UK ‘‘plug-andplay composable services’’ vision and the role of semantics in such an e-Infrastructure.
---
paper_title: Geospatial Cyberinfrastructure : Past , present and future

paper_content:

A Cyberinfrastructure (CI) is a combination of data resources, network protocols, computing platforms, and computational services that brings people, information, and computational tools together to perform science or other data-rich applications in this information-driven world. Most science domains adopt intrinsic geospatial principles (such as spatial constraints in phenomena evolution) for large amounts of geospatial data processing (such as geospatial analysis, feature relationship calculations, geospatial modeling, geovisualization, and geospatial decision support). Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains (such as the environmental and Earth sciences). GCI is based on recent advancements in geographic information science, information technology, computer networks, sensor networks, Web computing, CI, and e-research/e-science. This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Similar to how GIS transformed the procedures for geospatial sciences, GCI provides significant improvements to how the sciences that need geospatial information will advance. The evolution of GCI will produce platforms for geospatial science domains and communities to better conduct research and development and to better collect data, access data, analyze data, model and simulate phenomena, visualize data and information, and produce knowledge. To achieve these transformative objectives, col
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paper_title: Introduction to distributed geographic information processing research

paper_content:

Distributed geographic information processing (DGIP) refers to the processing of geographic information across dispersed processing units through computer networks and other communication channels. DGIP has become increasingly important in the past decade with the popularization of computer networks, the growth of distributed data repositories, and the collaboration of researchers, developers, and users among multiple disciplines using geographic information. DGIP focuses on the technical research on how to allocate and process geographic information resources in a distributed environment to achieve a specific application objective (such as the implementation of virtual globes). The geographic information resources may include sensors, geographic data, models, information, knowledge, visualization tools, computers, computer networks, software components, architecture, security strategies, applications, and human resources. This introduction to DGIP research defines six research areas: (a) DGIP architecture, including service-oriented architecture (SOA) and Federal Enterprise Architecture (FEA), (b) spatial computing issues for leveraging and allocating computing power to process geographic information, (c) geographic information-processing models for decoupling and integrating models for specific or cross application domains, (d) interoperability, defining the standards and interfaces for sharing processing units, (e) intelligence in DGIP for leveraging knowledge, and (f) applied sciences. The papers selected for this special issue cover all six areas. DGIP will become increasingly important with the globalization of our daily lives across planet Earth and the need to leverage distributed geographic information resources for problem solving and decision making in the global environment.
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paper_title: Users as essential contributors to spatial cyberinfrastructures

paper_content:

Current accounts of spatial cyberinfrastructure development tend to overemphasize technologies to the neglect of critical social and cultural issues on which adoption depends. Spatial cyberinfrastructures will have a higher chance of success if users of many types, including nonprofessionals, are made central to the development process. Recent studies in the history of infrastructures reveal key turning points and issues that should be considered in the development of spatial cyberinfrastructure projects. These studies highlight the importance of adopting qualitative research methods to learn how users work with data and digital tools, and how user communities form. The author's empirical research on data sharing networks in the Pacific Northwest salmon crisis at the turn of the 21st century demonstrates that ordinary citizens can contribute critical local knowledge to global databases and should be considered in the design and construction of spatial cyberinfrastructures.
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paper_title: Design and Implementation of GeoBrain Online Analysis System (GeOnAS)

paper_content:

GeOnAS is an extensible, scalable and powerful online geospatial analysis system based on Service Oriented Architecture (SOA), and is designed and implemented with the complementary technologies, Asynchronous JavaScript and XML (Ajax) and Web services, which greatly increase the interactive capabilities of graphical user interfaces and improve the user experience. It provides a highly interoperable way of accessing Open Geospatial Consortium (OGC) compliant web services for geospatial data discovery, retrieval, visualization and analysis, and leverages web service standards to enable service discovery, selection, negotiation and invocation for making more informed decisions.
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paper_title: Designing Service Architectures for Distributed Geoprocessing: Challenges and Future Directions

paper_content:

In this paper we study the feasibility of using services offered by a Spatial Data Infrastructure as a basis for distributed service oriented geoprocessing. By developing a prototype we demonstrate that a Spatial Data Infrastructure facilitates rapid development of applications that solve typical problems for an existing risk management application. The prototype provides users with a distributed application that enables the assessment of fire damage areas based on land cover data in a given area. The services involved in the application include: Web Feature Services, Web Map Services, a Gazetteer Service, a Catalogue Service, and Geoprocessing Services. We present the architecture of the application and describe details about implementation-specific issues. We conclude that current OGC specifications provide a sound basis for developing service oriented architectures for geographic applications; however, in particular for geoprocessing applications, we question the
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paper_title: What is cyberinfrastructure

paper_content:

Cyberinfrastructure is a word commonly used but lacking a single, precise definition. One recognizes intuitively the analogy with infrastructure, and the use of cyber to refer to thinking or computing -- but what exactly is cyberinfrastructure as opposed to information technology infrastructure? Indiana University has developed one of the more widely cited definitions of cyberinfrastructure. Cyberinfrastructure consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible. A second definition, more inclusive of scholarship generally and educational activities, has also been published and is useful in describing cyberinfrastructure: Cyberinfrastructure consists of computational systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible. In this paper, we describe the origin of the term cyberinfrastructure based on the history of the root word infrastructure, discuss several terms related to cyberinfrastructure, and provide several examples of cyberinfrastructure
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paper_title: Geospatial Cyberinfrastructure : Past , present and future

paper_content:

A Cyberinfrastructure (CI) is a combination of data resources, network protocols, computing platforms, and computational services that brings people, information, and computational tools together to perform science or other data-rich applications in this information-driven world. Most science domains adopt intrinsic geospatial principles (such as spatial constraints in phenomena evolution) for large amounts of geospatial data processing (such as geospatial analysis, feature relationship calculations, geospatial modeling, geovisualization, and geospatial decision support). Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains (such as the environmental and Earth sciences). GCI is based on recent advancements in geographic information science, information technology, computer networks, sensor networks, Web computing, CI, and e-research/e-science. This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Similar to how GIS transformed the procedures for geospatial sciences, GCI provides significant improvements to how the sciences that need geospatial information will advance. The evolution of GCI will produce platforms for geospatial science domains and communities to better conduct research and development and to better collect data, access data, analyze data, model and simulate phenomena, visualize data and information, and produce knowledge. To achieve these transformative objectives, col
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paper_title: Geospatial Cyberinfrastructure : Past , present and future

paper_content:

A Cyberinfrastructure (CI) is a combination of data resources, network protocols, computing platforms, and computational services that brings people, information, and computational tools together to perform science or other data-rich applications in this information-driven world. Most science domains adopt intrinsic geospatial principles (such as spatial constraints in phenomena evolution) for large amounts of geospatial data processing (such as geospatial analysis, feature relationship calculations, geospatial modeling, geovisualization, and geospatial decision support). Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains (such as the environmental and Earth sciences). GCI is based on recent advancements in geographic information science, information technology, computer networks, sensor networks, Web computing, CI, and e-research/e-science. This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Similar to how GIS transformed the procedures for geospatial sciences, GCI provides significant improvements to how the sciences that need geospatial information will advance. The evolution of GCI will produce platforms for geospatial science domains and communities to better conduct research and development and to better collect data, access data, analyze data, model and simulate phenomena, visualize data and information, and produce knowledge. To achieve these transformative objectives, col
---
paper_title: What is cyberinfrastructure

paper_content:

Cyberinfrastructure is a word commonly used but lacking a single, precise definition. One recognizes intuitively the analogy with infrastructure, and the use of cyber to refer to thinking or computing -- but what exactly is cyberinfrastructure as opposed to information technology infrastructure? Indiana University has developed one of the more widely cited definitions of cyberinfrastructure. Cyberinfrastructure consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible. A second definition, more inclusive of scholarship generally and educational activities, has also been published and is useful in describing cyberinfrastructure: Cyberinfrastructure consists of computational systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible. In this paper, we describe the origin of the term cyberinfrastructure based on the history of the root word infrastructure, discuss several terms related to cyberinfrastructure, and provide several examples of cyberinfrastructure
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Title: Geospatial Cyberinfrastructure and Geoprocessing Web — A Review of Commonalities and Differences of E-Science Approaches

Section 1: Introduction
Description 1: Provide an introduction to geospatial data growth, SDIs, and the advent of online geoprocessing, highlighting the need for e-Science environments.

Section 2: E-Science and the Geographic Information Domain
Description 2: Discuss the components and technologies of e-Science environments in the geographic information domain.

Section 3: The Geospatial Cyberinfrastructure
Description 3: Define the geospatial cyberinfrastructure, its components, technologies, and specific examples of its applications.

Section 4: The Geoprocessing Web
Description 4: Define the geoprocessing web, its components, technologies, and specific examples of its applications.

Section 5: Differences and Commonalities of the Approaches to E-Science
Description 5: Compare and contrast the geospatial cyberinfrastructure and the geoprocessing web in terms of user interaction, functions, and computing infrastructure.

Section 6: Characteristics of Two Representative Applications
Description 6: Provide a detailed comparison of ESA G-POD (GCI) and GeoBrain GeOnAS (geoprocessing web) focusing on user interaction, services, and computing infrastructures.

Section 7: Integration of Geospatial Cyberinfrastructure and Geoprocessing Web
Description 7: Discuss the potential integration of GCI and the geoprocessing web, highlighting complementary aspects and future e-Science environment developments.

Section 8: Conclusions
Description 8: Summarize the key findings, the overlapping aims of GCI and geoprocessing web, and the future outlook for integrated e-Science environments.