TOPIC E3 Advanced Information Systems Technology For Earth Science

• Search agents that support applications involving the use of NASA data;
  
• Methods that support the robust production of data products given a set of high-level goals and constraints;
  
• Autonomous data collection including the coordination of space or airborne platforms while adhering to a set of data collection goals and resource constraints;
  
• Autonomous data logging devices (software, or hardware and software) supporting a variety of weather and climate sensors, capable of ground-based operation in a wide variety of environmental conditions; such systems would probably be solar powered with accurate time stamping;
  
• Planning and scheduling methods related to Earth Science Mission objectives;
  
• System and subsystem health and maintenance, both space- and ground-based;
  
• Distributed decision making, using multiple agents, and/or mixed autonomous systems;
  
• Automated software testing;
  
• Verification and validation of automated systems;
  
• Automatic software generation and processing algorithms;
  
• Control of Field Programmable Gate-Arrays (FPGA) to provide real-time products.
  

• Core services (autonomous software systems) for automated, scalable, and reliable management of distributed, dynamic, and heterogeneous computing, data, and instrument resources. Services of interest (which may be based on Open Grid Service Infrastructure [OGSI]) include those for authentication and security, resource and service discovery, resource scheduling, event monitoring, uniform access to compute and data resources, and efficient and reliable data transfer.
  
• Higher level services, including those for job management, resource brokering, workflow management, portlet (i.e., application-specific graphical user interface [GUI]) building, and collaboration.
  
• Services for management of distributed, heterogeneous information, including replica management, intuitive interfaces, and instantiation on demand or “virtualized data.” These services would be used, for example, to access and manipulate NASA’s wealth of geospatial and remote sensing data.
  
• Science portals for cross-disciplinary discovery, understanding, and prediction, encapsulating services for single sign-on access, semantic resource and service discovery, workflow composition and management, remote collaboration, and results analysis and visualization.
  
• Tools for rapidly porting and hosting science applications in a distributed environment. These applications were written for an integrated, or workstation, environment using standard programming languages or tools such as Matlab, Interactive Data Language (IDL), or Mathematica.
  

• Tools for automating large-scale modeling, simulation, and analysis, including those for managing computational ensembles, performing model-optimization studies, interactive computational steering, and maintaining progress in long-running computations in spite of unreliable computing, data, and network resources.
  
• Tools for computer system performance modeling, prediction, and optimization for real applications.
  
• Techniques and tools for application parallelization and performance analysis.
  
• Tools for effective load balancing, and high reliability, availability, and serviceability (RAS) in commodity clusters and other large-scale computing systems.
  
• Novel supercomputing approaches using FPGAs, graphics processors, and other novel architectures and technologies.
  

• Unique, innovative data reduction, rapid analysis and data exploitation methodologies and algorithms of information from remotely sensed data sets, e.g., automated feature extraction, data mining, etc.;
  
• Algorithms and approaches to enable the efficient production of data products from active imaging systems, e.g., multipoint data resampling, digital elevation model creation, etc.;
  
• Data merge and fusion software for efficient production and real-time delivery of digital products of ESE Mission and other remote sensing data sets, e.g., weather observation and land use and land cover data sets;
  
• Innovative approaches for incorporation of GPS data into in situ data collection operations with dynamic links to spatial databases including environmental models
  
• Image enhancement algorithms for improving spatial, spectral, and geometric image attributes;
  
• Innovative approaches for the querying and assimilation of application-specific datasets from disparate and distributed databases from government, academic and commercial sources into a common framework for data analysis
  
• Innovative approaches for querying of application-specific data sets from disparate, distributed databases in government, academic, and commercial data warehouses into a common framework for data analysis; and
  
• Innovative visualization technologies contributing to the analysis of data through the display and visualization of some or all of the above data types including providing the linkages and user interface between the cartographic model and attribute databases.
  

• Develop technologies that support long term data management, storage, search, and retrieval of very large, distributed, geospatial Earth science data sets, including the development of object based storage devices, file systems that promote long term data maintenance and recovery from user errors, and global compression techniques that optimize data backup operations.
  
• Develop techniques to manage and locate data in a distributed metadata catalog environment and provide tools to create, use, and then tear down wide area high speed Storage Area Network (SAN) access to remote data sets.
  
• Develop tools and techniques that enable high bandwidth scientific collaboration in a distributed environment, and allow data viewing, real-time data browse, and general purpose rendering of multivariate geospatial scientific data sets using georectification, data overlays, data reduction, and data encoding across widely differing data types and formats.
  
• Design and implement 3-D virtual reality environments for scientific data visualization that will enable users to 'fly' through the data space to locate specific areas of interest, and make use of novel 3-D presentation techniques which minimize or eliminate the need for special user devices such as goggles or helmets.
  

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