Ken Keiser

Syntactic and semantic metadata integration for science data use

This paper proposes a novel metadata solution to allow applications to intelligently use science data in an automated fashion. The solution provides rich syntactic and semantic metadata, where the semantic metadata is linked with an ontology to define the semantic terms. This solution allows applications to exploit the syntactic metadata to read the data and the semantic metadata to infer the content and the meaning of the data. The solution presented in this paper leverages the Earth Science Markup Language for providing the syntactic metadata and adds a semantic metadata component along with links to the appropriate ontology. This new semantic component is orthogonal to the syntactic metadata, so it does not perturb the existing design. An example application was designed and built that integrates this syntactic and semantic metadata via an ontology to perform a data processing operation.

Cyber Security for Additive Manufacturing

This paper describes the cyber security implications of additive manufacturing (also known as 3-D printing). Three-D printing has the potential to revolutionize manufacturing and there is substantial concern for the security of the storage, transfer and execution of 3-D models across digital networks and systems. While rapidly gaining in popularity and adoption by many entities, additive manufacturing is still in its infancy. Supporting the broadest possible applications the technology will demand the ability to demonstrate secure processes from ideas, design, prototyping, production and delivery. As with other technologies in the information revolution, additive manufacturing technology is at risk of outpacing a competent security infrastructure so research and solutions need to be tackled in concert with the 3-D boom.

The US IOOS Coastal and Ocean Modeling Testbed for advancing research to applications

Coastal waters and lowlands of the U.S. are threatened by climate change, sea-level rise, flooding, oxygen depleted “dead zones”, oil spills and unforeseen disasters. With funding from U.S. Integrated Ocean Observing System (IOOS®), the Southeast University Research Association (SURA) facilitated strong and strategic collaborations among experts from academia, federal operational centers and industry and guided the U.S. IOOS Coastal and Ocean Modeling Testbed (COMT) through its successful pilot phase. The focus of this paper is the development of the cyberinfrastructure, including successes and challenges during this pilot phase of the COMT. This is the first testbed intended to serve multiple federal agencies and be focused on the coastal ocean and Great Lakes. National Oceanic and Atmospheric Administrations (NOAA) National Center for Environmental Prediction (NCEP) has offered an operational base for the COMT, which addresses NCEP modeling challenges in coastal predictions by enabling the transition of research improvements into NCEPs operational forecast capability. Additional Federal participants include Navy, U.S. Geological Survey (USGS), Environmental Protection Agency and the U.S. Army Corps of Engineers (USACE). The mission of the Coastal and Ocean Modeling Testbed (COMT) is to use targeted research and development to accelerate the transition of scientific and technical advances from the coastal and ocean modeling research community to improve identified operational ocean products and services (i.e. via research to applications and also applications to research). The vision of the program is to enhance the accuracy, reliability, and scope of the federal suite of operational ocean modeling products, while ensuring its user community is better equipped to solve challenging coastal problems and recognize the COMT to be where the best coastal science is operationalized. Since its initiation in June, 2010, the COMT has developed to include a flexible and extensible community research framework to test and evaluate predictive models to address key coastal environmental issues. Initially, the COMT addressed three general research challenges of socioeconomic relevance: estuarine hypoxia, shelf hypoxia, and coastal inundation. A cyberinfrastructure was developed to facilitate model assessment based on community standards, including a distributed data repository, automated cataloging mechanism, quick browse facility, and tools for flexible and detailed scientific investigation of both model output and data. Models, tools and techniques from the Testbed are starting to be incorporated into the NOAA research and operational frameworks, reducing the transition time from research to federal operations. Ultimately, the COMT has had many successes as a pilot project and provides an effective and efficient environment for coordinating and improving coastal ocean and Great Lakes modeling efforts needed by the federal operational forecasting community.

Mining Scientific Data using the Internet as the Computer

This paper describes approaches and methodologies facilitating the analysis of large amounts of distributed scientific data. The existence of full-featured analysis tools, such as the Algorithm Development and Mining (ADaM) toolkit and online data repositories now provide easy access and analysis capabilities to large amounts of data. However, there are obstacles to getting the analysis tools and the data together in a workable environment. Does one bring the data to the tools or deploy the tools close to the data? The large size of many current Earth science datasets incurs significant overhead in network transfer for analysis workflows, even with the current advanced networking capabilities. We are developing two solutions for this problem that address different analysis scenarios. The first is a Data Center Deployment of the analysis services for large data selections, orchestrated by a remotely defined analysis workflow. The second is a Data Mining Center approach of providing a cohesive analysis solution for smaller subsets of data. The two approaches can be complementary and thus provide flexibility for researchers to exploit the best solution for their data requirements.

SCOOP Data Management: A Standards-based Distributed System for Coastal Data and Modeling

The Southeastern Universities Research Association (SURA) coastal ocean observing and prediction (SCOOP) program is a SURA Coastal Research initiative that is deploying cutting edge information technology to advance the science of environmental prediction and hazard planning for our nations coasts. SCOOP is a distributed program, incorporating heterogeneous data, software and hardware; thus the use of standards to enable interoperability is key to SCOOPs success. Standards activities range from internal coordination among SCOOP partners to participation in national standards efforts. As the lead partner in the SCOOP program for both data management and data translation, the University of Alabama in Huntsville (UAH) is developing a suite of advanced technologies to provide core data and information management services for scientific data, including the SCOOP Catalog and a suite of standards-based web services providing Catalog access. Currently under development is a web service that will export information on SCOOP data collections in a schema compliant with the Federal Geographic Data Committees Content Standard for Digital Geospatial Metadata. SCOOP is also a participant in the OpenlOOS Interoperability Demonstration, which leverages open geospatial consortium (OGQ standards such as the Web map service (WMS) and Web Feature Service (WFS) protocols to display near real time coastal observations together with water level, wave, and surge forecasts. SCOOP partners are also active participants in several data and metadata standards efforts, including the national ocean sciences data management and communications metadata studies and the marine metadata interoperability project. Continued close cooperation between the IT and coastal science modeling communities is producing positive results toward a real-time modeling environment that will benefit coastal stakeholders through better predictive capabilities.

Regional Air-Sea interactions (RASI) climatology for central america coastal gap wind and upwelling events

Coastal gap wind jets and associated ocean upwelling events present significant regional Air-Sea interaction over the eastern Pacific warm pool. Besides the regional weather impact, the ocean upwelling events also significantly change the nutrient distribution over the area by bringing up deep cold water and nutrients to the ocean surface. These events are therefore of interest to both research and commercial users. In this paper, we present automated algorithms to extract gap wind and upwelling events from satellite-derived data products. An ocean upwelling event is characterized by a sudden and significant decrease in sea surface temperature (SST). This study focuses on three special locations: the Gulf of Tehuantepec, Gulf of Papagayo and Gulf of Panama, where gap wind events are often observed during northern hemisphere winter months. The information acquired from this automated detection is collected and managed by the Global Hydrology Resource Center (GHRC), a NASA Distributed Active Archive Center (DAAC), located in Huntsville, AL, and is made publicly available to users.

Building a Climatology of Mountain Gap Wind Jets and Related Coastal Upwelling

Winds accelerating through coastal topology are capable of generating jets that often result in cold-water upwelling events in near-coast locations. In situ measurements are frequently not available in remote locations for many of the mountain gap locations globally, so to provide a record of these events for researchers, as well as military and commercial interests, this NASA-funded project is demonstrating how remotely sensed satellite data derived products, and fused model and observations, for wind and sea surface temperatures can be used to detect both wind jet and upwelling events. An algorithm was developed to automatically detect gap wind and ocean upwelling events at gulf regions of Central America using the Cross-Calibrated, Multi-Platform (CCMP) ocean surface wind product and the Optimally Interpolated Sea Surface Temperature (OISST) product. Hierarchical thresholding and region growing methods are used to extract regions of strong winds and temperature anomalies. A post processing step further links the detected events to generate time series of these events. Though developed for Central America regions, the algorithm is being extended to apply to other coastal regions so that detected event products are globally consistent. Through collaboration with the Global Hydrology Resource Center (GHRC), a NASA Distribute Active Archive Center, this project is analyzing large climate data records to generate a resulting climatology of wind jet and upwelling events at known geographic locations will be available as a resource for other researchers. Likewise, through integration of the projects analysis techniques with the GHRCs data ingest processing, the identification and notification of new or current events will likewise be openly available to research, commercial and military users. This paper provides a report on the preliminary results of applying the teams approach of identifying and capturing events for selected mountain gap jet locations.

SCOOP Data Management: A Standards-Based Distributed Information System for Coastal Data Management

The Southeastern Universities Research Association (SURA) Coastal Ocean Observing and Prediction (SCOOP – http://scoop.sura.org) program is a SURA Coastal Research initiative that is deploying cutting edge IT to advance the science of environmental prediction and hazard planning for our nation’s coasts. SCOOP is intended as a working prototype infrastructure to serve as a model for a distributed Integrated Ocean Observing System (IOOS) in the southeastern region (Bogden and Graves 2005). IOOS is a national initiative to create a new system for collecting and disseminating information about the oceans. The system will support a variety of practical applications, along with enabling research.