Shawn Hampton

Active management of scientific data

Sophisticated data-distribution schemes and recent developments in sensors and instruments that can monitor the lower kilometers of the atmosphere at high levels of resolution have rapidly expanded the quantity of information available to mesoscale meteorology. The myLEAD personalized information-management tool helps geoscience users make sense of this vastly expanded information space. MyLEAD extends the general globus metadata catalog service and leverages a well-known general and extensible schema. Its orientation makes it an active player in large-scale distributed computation environments characterized by interacting grid and Web services.

Architectural Overview of MAEviz – HAZTURK

MAEviz is a broadly extensible, open source platform for earthquake hazard risk management. MAEviz is a model cyberenvironment that provides practical capabilities for researchers through decision-makers to model earthquake events, develop risk reduction strategies, and implement mitigation plans to minimize the impact of earthquake disasters while also providing a pathway for researchers to quickly add new algorithms and data to assure that decisions are based on state-of-the-art engineering understanding. While MAEviz is capable of interacting with remote data and computational sources, it is also fully capable of running analyses locally so research scientists and decision-makers can generate information when a catastrophic event occurs and provide first-responders result information. This article describes MAEvizs overall layered architecture, its foundation in the widely used Eclipse Rich Client Platform (RCP), and use of open-source middleware and geographic information system (GIS) components. MAEvizs data management capabilities and workflow-oriented execution model are also discussed with an emphasis on detailing MAEvizs capability to incorporate new data types and new analysis modules.

Overview and applications of Maeviz-Hazturk 2007

A new generation of tools is needed to allow researchers and practicing engineers the ability to leverage investments in new methodologies and software infrastructure while enabling customization to local conditions. MAEviz represents such a next generation of seismic risk assessment software, based on the Mid-America Earthquake (MAE) Center research in Consequence-based Risk Management (CRM) and is designed to be extended, customized, and evolved to meet the needs of specific organizations and regions. MAEviz helps bridge the gap between researchers, practitioners, and policy-makers by integrating the latest research findings and most accurate data, using state-of-the-art methodologies, in an extensible software platform.

An Authorization Framework for a Grid Based Component Architecture

This paper presents an architecture to meet the needs for authentication and authorization in Grid based component systems. While Grid Security Infrastructure (GSI) [1] is accepted as the standard for authentication on the Grid, distributed authorization is still an open problem being investigated by various groups [2],[3],[4]. Our design provides authentication and fine-grained authorization at the interface, method and parameter levels. We discuss the ways in which internal and external authorization services can be used in a component framework. The design is flexible to allow the use of various existing policy languages and authorization systems. Our prototype is based on XCAT, an implementation of the Common Component Architecture (CCA) specification.

MAEviz: Bridging the Time-from-Discovery Gap between Seismic Research and Decision Making

MAEviz is an open-source project that helps reduce the time from discovery gap that exists between researchers, practitioners, and decision makers by integrating the latest research findings, most accurate data, and new methodologies into a single software product. It was developed as a platform to perform seismic risk assessment based on the mid-america earthquake (MAE) center research in the consequence-based risk management (CRM) framework. MAEviz is built upon an open source, extensible software platform developed at NCSA using the eclipse rich client platform (RCP). The example shown in the poster is network-based seismic retrofit (NBSR) analysis. The analysis solves a typical problem faced by decision makers that, given a fixed budget, which combination of bridges and retrofit methods would minimize the societal cost of an earthquake. It clearly shows how new science can be put quickly into the hands of the decision makers, thus bridging the gap between research and practical application. MAEviz is shown to be a powerful tool that can currently be used to assist decision makers in preparing for and mitigating the consequences of seismic hazards. Moreover, the extensible architecture of MAEviz allows it to be easily adapted to integrate newly discovered science and data, both in the area of seismic risk assessment, as well as other future research areas.

On the Use of Services to Support Numerical Weather Prediction

The challenges of building an effective grid-based problem solving environment that truly extends and embraces a computational scientist’s traditional tools are multifold. It is far too easy to build simple stovepipes that allow fixed use patterns, that don’t extend a scientist’s desktop, and fail to encompass the full range of patterns that a scientist needs to find such a problem-solving environment as a liberating and enabling tool. In the LEAD project, we have focused on the most challenging users of numerical weather prediction, namely, the atmospheric science researchers, who are prone to use their own tools, their own modified versions of community codes such as the Weather Research and Forecasting (WRF) model, and are typically comfortable with elaborate shell scripts to perform the work they find to be necessary to succeed, to drive our development efforts. Our response to these challenges includes a multi-level workflow engine, to handle both the challenges of ensemble description and execution, as well as the detailed patterns of workflow on each computational resource; services to support the peculiarities of each platform being used to do the modeling (such as on TeraGrid), and the use of an RDF triple store and message bus together as the backbone of our notification, logging, and metadata infrastructure. The design of our problem-solving environment elements attempts to come to grips with lack of control of elements surrounding and supporting the environment; we achieve this through multiple mechanisms including using the OSGI plug-in architecture, as well as the use of RDF triples as our finest-grain descriptive element. This combination, we believe, is an important stepping stone to building a cyber environment, which aims to provide flexibility and ease of use far beyond the current range of typical problem solving environments.