Using a combination of high end computing and machine learning algorithms we developed a system to interrogate climate reanalysis data in a species distribution model. The results show that this system can be used as a tool to identify key variables of interest relevant to a species and to generate a probability map of the distribution of a species of interest. This opens new avenues for statistical inference in regions with sparse observational data.

NASA Wrangler is a loosely-coupled, event driven, highly parallel data aggregation service designed to take advantage of the elastic resource capabilities of cloud computing. Wrangler automatically collects Earth observational data, climate model outputs, derived remote sensing data products, and historic biophysical data for pre-, active-, and post-wildfire decision making. It is a core service of the RECOVER decision support system, which is providing rapid-response GIS analytic capabilities to state and local government agencies. Wrangler reduces to minutes the time needed to assemble and deliver crucial wildfire-related data.

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.

The NASA Center for Climate Simulation (NCCS) plays a lead role in meeting the computational and data management requirements of climate modeling and data assimilation. Scientific data services are becoming an important part of the NCCS mission. The NCCS Data Management System (DMS) is a key element of NCCS's technological response to this expanding role. In DMS, we are using the Integrated Rule-Oriented Data System (iRODS) to combine disparate data collections into a federated platform upon which various data services can be implemented. Work to date has demonstrated the effectiveness of iRODS in managing a large-scale collection of observational data, managing model output data in a cloud computing context, and managing NCCS-hosted data products that are published through community-defined services such as the Earth System Grid (ESG). Plans call for staged operational adoption of iRODS in the NCCS.

The USGS and NASA, in conjunction with Colorado State University, George Mason University and other partners, have developed the Invasive Species Forecasting System (ISFS), a flexible tool that capitalizes on NASA's remote sensing resource to produce dynamic habitat maps of invasive terrestrial plant species across the United States. In 2006 ISFS was adopted to generate predictive invasive habitat maps to benefit noxious plant and fire management teams in three major National Park systems: The Greater Yellowstone Area (Yellowstone / Grand Tetons National Parks), Sequoia and Kings Canyon National Park, and interior Alaskan (between Denali, Gates of The Arctic and Yukon-Charley). One of the objectives of this study is to explore how the ISFS enhances decision support apparatus in use by National Park management teams. The first step with each park system was to work closely with park managers to select top-priority invasive species. Specific species were chosen for each study area based on management priorities, availability of observational data, and their potential for invasion after fire disturbances. Once focal species were selected, sources of presence/absence data were collected from previous surveys for each species in and around the Parks. Using logistic regression to couple presence/absence points with environmental data layers, the first round of ISFS habitat suitability maps were generated for each National Park system and presented during park visits over the summer of 2006. This first engagement provided a demonstration of what the park service can expect from ISFS and initiated the ongoing dialog on how the parks can best utilized the system to enhance their decisions related to invasive species control. During the park visits it was discovered that separate "expert opinion" maps would provide a valuable baseline to compare against the ISFS model output. Opinion maps are a means of spatially representing qualitative knowledge into a quantitative two-dimensional map. Furthermore, our approach combines the qualitative expert opinion habitat maps with the quantitative ISFS habitat maps in a difference map that shows where the two maps agree and disagree. The objective of the difference map is to help focus future field sampling and improve model results. This paper presents a demonstration of the habitat, expert opinion, and difference map for Yellowstone National Park.

The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being considered by NASA's Earth Science Vision for 2025. The invasive species problem is complex and presents many challenges. Developing an invasive species predictive capability could significantly advance the science and technology of ecological forecasting.

The areas of application of hypermedia technology, combined with the capabilities that hypermedia provides for manipulating structure, create an environment in which version control is very important. A hypermedia version control framework has been designed to specifically address the version control problem in open hypermedia environments. One of the primary distinctions of the framework is the partitioning of hypermedia version control functionality into intrinsic and application-specific categories. The version control has been used as a model for the design of version control services for a hyperbase management system that provides complete version support for both data and structural entities. In addition to serving as a version control model for open hypermedia environments, the framework offers a clarifying and unifying context in which to examine the issues of version control in hypermedia.

Agency and hypermedia have both been suggested as powerful means to cope with future information management and human-computer interaction requirements. However, research projects have included interface agents only marginally in the context of hypermedia systems. This article proposes a set of criteria for characterizing interface agents and offers a perspective view of ongoing research in the field using those criteria as a framework. The need to provide a supporting infrastructure that facilitates testing and experimentation of interface agents is stressed. The article describes an existing open hypermedia architecture and introduces an extended architecture that includes provisions to support the development and operation of interface agents. A prototype instantiating this system architecture is presented, as well as an initial assessment of the potential and requirements of interface agents in a hypermedia environment.

Many important issues in the design and implementation of hypermedia system functionality focus on the way interobject connections are represented, manipulated, and stored. A prototypic system called HB1 is being designed to meet the storage needs of next-generation hypermedia system architectures. HB1 is referred to as a hyperbase management system (HBMS) because it supports, not only the storage and manipulation of information, but the storage and manipulation of the connectivity data that link information together to form hypermedia. Among HB1's distinctions is its use of a semantic network database system to manage physical storage. Here, basic semantic modeling concepts as they apply to hypermedia systems are reviewed, and experiences using a semantic database system in HB1 are discussed. Semantic data models attempt to provide more powerful mechanisms for structuring objects than are provided by traditional approaches. In HB1, it was necessary to abstract interobject connectivity, behaviors, and information for hypermedia. Building on top of a semantic database system facilitated such a separation and made the structural aspects of hypermedia conveniently accessible to manipulation. This becomes particularly important in the implementation of structure-related operations such as structural queries. Our experience suggests that an integrated semantic object-oriented database paradigm appears to be superior to purely relational, semantic, or object-oriented methodologies for representing the structurally complex interrelationships that arise in hypermedia.