Sara J. Graves

Service-Oriented Environments for Dynamically Interacting with Mesoscale Weather

Within a decade after John von Neumann and colleagues conducted the first experimental weather forecast on the ENIAC computer in the late 1940s, numerical models of the atmosphere become the foundation of modern-day weather forecasting and one of the driving application areas in computer science. This article describes research that is enabling a major shift toward dynamically adaptive responses to rapidly changing environmental conditions.

Mapping the biosphere: Exploring species to understand the origin, organization and sustainability of biodiversity

The time is ripe for a comprehensive mission to explore and document Earths species. This calls for a campaign to educate and inspire the next generation of professional and citizen species explorers, investments in cyber-infrastructure and collections to meet the unique needs of the producers and consumers of taxonomic information, and the formation and coordination of a multi-institutional, international, transdisciplinary community of researchers, scholars and engineers with the shared objective of creating a comprehensive inventory of species and detailed map of the biosphere. We conclude that an ambitious goal to describe 10 million species in less than 50 years is attainable based on the strength of 250 years of progress, worldwide collections, existing experts, technological innovation and collaborative teamwork. Existing digitization projects are overcoming obstacles of the past, facilitating collaboration and mobilizing literature, data, images and specimens through cyber technologies. Charting the biosphere is enormously complex, yet necessary expertise can be found through partnerships with engineers, information scientists, sociologists, ecologists, climate scientists, conservation biologists, industrial project managers and taxon specialists, from agrostologists to zoophytologists. Benefits to society of the proposed mission would be profound, immediate and enduring, from detection of early responses of flora and fauna to climate change to opening access to evolutionary designs for solutions to countless practical problems. The impacts on the biodiversity, environmental and evolutionary sciences would be transformative, from ecosystem models calibrated in detail to comprehensive understanding of the origin and evolution of life over its 3.8 billion year history. The resultant cyber-enabled taxonomy, or cybertaxonomy, would open access to biodiversity data to developing nations, assure access to reliable data about species, and change how scientists and citizens alike access, use and think about biological diversity information.

CASA and LEAD: adaptive cyberinfrastructure for real-time multiscale weather forecasting

Two closely linked projects aim to dramatically improve storm forecasting speed and accuracy. CASA is creating a distributed, collaborative, adaptive sensor network of low-power, high-resolution radars that respond to user needs. LEAD offers dynamic workflow orchestration and data management in a Web services framework designed to support on-demand, real-time, dynamically adaptive systems

Towards dynamically adaptive weather analysis and forecasting in LEAD

LEAD is a large-scale effort to build a service-oriented infrastructure that allows atmospheric science researchers to dynamically and adaptively respond to weather patterns to produce better-than-real time predictions of tornadoes and other “mesoscale” weather events. In this paper we discuss an architectural framework that is forming our thinking about adaptability and give early solutions in workflow and monitoring.

ADaM: a data mining toolkit for scientists and engineers

Algorithm Development and Mining (ADaM) is a data mining toolkit designed for use with scientific data. It provides classification, clustering and association rule mining methods that are common to many data mining systems. In addition, it provides feature reduction capabilities, image processing, data cleaning and preprocessing capabilities that are of value when mining scientific data. The toolkit is packaged as a suite of independent components, which are designed to work in grid and cluster environments. The toolkit is extensible and scalable, and has been successfully used in several diverse data mining applications. ADaM has also been used in conjunction with other data mining toolkits and with point tools. This paper presents the architecture and design of the ADaM toolkit and discusses its application in detecting cumulus cloud fields in satellite imagery.

Using association rules as texture features

A new type of texture feature based on association rules is proposed in this paper. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. Association rules capture both structural and statistical information, and automatically identifies the structures that occur most frequently and relationships that have significant discriminative power. Methods for classification and segmentation of textured images using association rules as texture features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. It is shown that association rule features can distinguish texture pairs with identical first, second, and third order statistics, and texture pairs that are not easily discriminable visually.

Real-time storm detection and weather forecast activation through data mining and events processing

Each year across the USA, destructive weather events disrupt transportation and commerce, resulting in both loss of lives and property. Mitigating the impacts of such severe events requires innovative new software tools and cyberinfrastructure through which scientists can monitor data for specific severe weather events such as thunderstorms and launch focused modeling computations for prediction and forecasts of these evolving weather events. Bringing about a paradigm shift in meteorology research and education through advances in cyberinfrastructure is one of the key research objectives of the Linked Environments for Atmospheric Discovery (LEAD) project, a large-scale, interdisciplinary NSF funded project spanning ten institutions. In this paper we address the challenges of making cyberinfrastructure frameworks responsive to real-time conditions in the physical environment driven by the use cases in mesoscale meteorology. The contribution of the research is two-fold: on the cyberinfrastructure side, we propose a model for bridging between the physical environment and e-Science1 workflow systems, specifically through events processing systems, and provide a proof of concept implementation of that model in the context of the LEAD cyberinfrastructure. On the algorithmic side, we propose efficient stream mining algorithms that can be carried out on a continuous basis in real time over large volumes of observational data.

Earth Science Markup Language (ESML): a solution for scientific data-application interoperability problem

Interchange technologies facilitate seamless interactions between applications, tools and services with datasets in heterogeneous formats. The Information Technology and Systems Center at the University of Alabama in Huntsville is developing an interchange technology focused on scientific data in general, and particularly on the vast amounts of remotely sensed Earth Science data. This interchange technology consists of the Earth Science Markup Language (ESML) and a related library of programming utilities. ESML, based on the eXtensible Markup Language (XML), allows data format structure descriptions to be written in a standard manner. ESML is unique in that it is not another new data format, instead it is a external structural metadata based solution for decoding existing formats. The effort involved to describe legacy data formats in ESML is small. ESML and the associated software library will allow wider interoperability of Earth Science services and tools, enabling Earth Scientists to work more easily with data in a variety of formats. This interchange technology will facilitate the development of data format independent search, visualization, and analysis tools. This paper will describe this interchange technology and compare it with similar XML based efforts.

Architecture of a Community Infrastructure for Predicting and Analyzing Coastal Inundation

The Southeastern Universities Research Association (SURA) has advanced the SURA Coastal Ocean Observing and Prediction (SCOOP) program as a multi-institution collaboration to design and prototype a modular, distributed system for real-time prediction and visualization of the coastal impacts from extreme atmospheric events, including hurricane inundation and waves. The SCOOP program vision is a community “cyberinfrastructure” that enables advances in the science of environmental prediction and coastal hazard planning. The system architecture is a coordinated and distributed network of interoperable, modularized components that include numerical models, information catalogs, distributed archives, computing resources, and network infrastructure. The components are linked over the Internet by standardized web-service interfaces in a service-oriented architecture (SOA). The design philosophy allows geographically disparate partnering institutions to provide complementary data-provider and integration services. The overall system enables coordinated sharing of resources, tools, and ideas among a virtual community of coastal and computer scientists. The distributed design builds on the notion that standards enable innovation, and seeks to leverage successes of the World Wide Web by creating an environment that nurtures interaction between the research community, the private sector, and government agencies working together on behalf of the nation.

Image segmentation using association rule features

A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.