The display of electronic commerce within virtual environments

In today’s competitive business environment, the majority of companies are expected to be represented on the Internet in the form of an electronic commerce site. In an effort to keep up with current business trends, certain aspects of interface design such as those related to navigation and perception may be overlooked. For instance, the manner in which a visitor to the site might perceive the information displayed or the ease with which they navigate through the site may not be taken into consideration. This paper reports on the evaluation of the electronic commerce sites of three different companies, focusing specifically on the human factors issues such as perception and navigation. Heuristic evaluation, the most popular method for investigating user interface design, is the technique employed to assess each of these sites. In light of the results from the analysis of the evaluation data, virtual environments are suggested as a way of improving the navigation and perception display constraints

IVOA Recommendation: VOResource: an XML Encoding Schema for Resource Metadata Version 1.03

This document describes an XML encoding standard for IVOA Resource Metadata, referred to as VOResource. This schema is primarily intended to support interoperable registries used for discovering resources; however, any application that needs to describe resources may use this schema. In this document, we define the types and elements that make up the schema as representations of metadata terms defined in the IVOA standard, Resource Metadata for the Virtual Observatory [Hanicsh et al. 2004]. We also describe the general model for the schema and explain how it may be extended to add new metadata terms and describe more specific types of resources

AstroGrid-D: Grid Technology for Astronomical Science

We present status and results of AstroGrid-D, a joint effort of astrophysicists and computer scientists to employ grid technology for scientific applications. AstroGrid-D provides access to a network of distributed machines with a set of commands as well as software interfaces. It allows simple use of computer and storage facilities and to schedule or monitor compute tasks and data management. It is based on the Globus Toolkit middleware (GT4). Chapter 1 describes the context which led to the demand for advanced software solutions in Astrophysics, and we state the goals of the project. We then present characteristic astrophysical applications that have been implemented on AstroGrid-D in chapter 2. We describe simulations of different complexity, compute-intensive calculations running on multiple sites, and advanced applications for specific scientific purposes, such as a connection to robotic telescopes. We can show from these examples how grid execution improves e.g. the scientific workflow. Chapter 3 explains the software tools and services that we adapted or newly developed. Section 3.1 is focused on the administrative aspects of the infrastructure, to manage users and monitor activity. Section 3.2 characterises the central components of our architecture: The AstroGrid-D information service to collect and store metadata, a file management system, the data management system, and a job manager for automatic submission of compute tasks. We summarise the successfully established infrastructure in chapter 4, concluding with our future plans to establish AstroGrid-D as a platform of modern e-Astronomy.Comment: 14 pages, 12 figures Subjects: data analysis, image processing, robotic telescopes, simulations, grid. Accepted for publication in New Astronom

Updated version of final design and of the architecture of SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Panel on future challenges in modeling methodology

This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing

A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area