SEARCH · Search NASA

Results for “web services”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

NASA NTRS2006

The Virtual Space Physics Observatory: Quick Access to Data and Tools

The Virtual Space Physics Observatory (VSPO; see http://vspo.gsfc.nasa.gov) has grown to provide a way to find and access about 375 data products and services from over 100 spacecraft/observatories in space and solar physics. The datasets are mainly chosen to be the most requested, and include most of the publicly available data products from operating NASA Heliophysics spacecraft as well as from solar observatories measuring across the frequency spectrum. Service links include a "quick orbits" page that uses SSCWeb Web Services to provide a rapid answer to questions such as "What spacecraft were in orbit in July 1992?" and "Where were Geotail, Cluster, and Polar on 2 June 2001?" These queries are linked back to the data search page. The VSPO interface provides many ways of looking for data based on terms used in a registry of resources using the SPASE Data Model that will be the standard for Heliophysics Virtual Observatories. VSPO itself is accessible via an API that allows other applications to use it as a Web Service; this has been implemented in one instance using the ViSBARD visualization program. The VSPO will become part of the Space Physics Data Facility, and will continue to expand its access to data. A challenge for all VOs will be to provide uniform access to data at the variable level, and we will be addressing this question in a number of ways.

Cornwell, Carl↗

NASA NTRS2005

A Reliable Service-Oriented Architecture for NASA's Mars Exploration Rover Mission

The Collaborative Information Portal (CIP) was enterprise software developed jointly by the NASA Ames Research Center and the Jet Propulsion Laboratory (JPL) for NASA's highly successful Mars Exploration Rover (MER) mission. Both MER and CIP have performed far beyond their original expectations. Mission managers and engineers ran CIP inside the mission control room at JPL, and the scientists ran CIP in their laboratories, homes, and offices. All the users connected securely over the Internet. Since the mission ran on Mars time, CIP displayed the current time in various Mars and Earth time zones, and it presented staffing and event schedules with Martian time scales. Users could send and receive broadcast messages, and they could view and download data and image files generated by the rovers' instruments. CIP had a three-tiered, service-oriented architecture (SOA) based on industry standards, including J2EE and web services, and it integrated commercial off-the-shelf software. A user's interactions with the graphical interface of the CIP client application generated web services requests to the CIP middleware. The middleware accessed the back-end data repositories if necessary and returned results for these requests. The client application could make multiple service requests for a single user action and then present a composition of the results. This happened transparently, and many users did not even realize that they were connecting to a server. CIP performed well and was extremely reliable; it attained better than 99% uptime during the course of the mission. In this paper, we present overviews of the MER mission and of CIP. We show how CIP helped to fulfill some of the mission needs and how people used it. We discuss the criteria for choosing its architecture, and we describe how the developers made the software so reliable. CIP's reliability did not come about by chance, but was the result of several key design decisions. We conclude with some of the important lessons we learned form developing, deploying, and supporting the software.

Mak, Ronald↗

NASA NTRS2011

A New User Interface for On-Demand Customizable Data Products for Sensors in a SensorWeb

A SensorWeb is a set of sensors, which can consist of ground, airborne and space-based sensors interoperating in an automated or autonomous collaborative manner. The NASA SensorWeb toolbox, developed at NASA/GSFC in collaboration with NASA/JPL, NASA/Ames and other partners, is a set of software and standards that (1) enables users to create virtual private networks of sensors over open networks; (2) provides the capability to orchestrate their actions; (3) provides the capability to customize the output data products and (4) enables automated delivery of the data products to the users desktop. A recent addition to the SensorWeb Toolbox is a new user interface, together with web services co-resident with the sensors, to enable rapid creation, loading and execution of new algorithms for processing sensor data. The web service along with the user interface follows the Open Geospatial Consortium (OGC) standard called Web Coverage Processing Service (WCPS). This presentation will detail the prototype that was built and how the WCPS was tested against a HyspIRI flight testbed and an elastic computation cloud on the ground with EO-1 data. HyspIRI is a future NASA decadal mission. The elastic computation cloud stores EO-1 data and runs software similar to Amazon online shopping.

Mandl, Daniel↗

NASA NTRS2015

Climate Model Diagnostic Analyzer

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

cloud computing↗

NASA NTRS2014

Proto-Examples of Data Access and Visualization Components of a Potential Cloud-Based GEOSS-AI System

Once a research or application problem has been identified, one logical next step is to search for available relevant data products. Thus, an early component of a potential GEOSS-AI system, in the continuum between observations and end point research, applications, and decision making, would be one that enables transparent data discovery and access by users. Such a component might be effected via the systems data agents. Presumably, some kind of data cataloging has already been implemented, e.g., in the GEOSS Common Infrastructure (GCI). Both the agents and cataloging could also leverage existing resources external to the system. The system would have some means to accept and integrate user-contributed agents. The need or desirability for some data format internal to the system should be evaluated. Another early component would be one that facilitates browsing visualization of the data, as well as some basic analyses.Three ongoing projects at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provide possible proto-examples of potential data access and visualization components of a cloud-based GEOSS-AI system. 1. Reorganizing data archived as time-step arrays to point-time series (data rods), as well as leveraging the NASA Simple Subset Wizard (SSW), to significantly increase the number of data products available, at multiple NASA data centers, for production as on-the-fly (virtual) data rods. SSWs data discovery is based on OpenSearch. Both pre-generated and virtual data rods are accessible via Web services. 2. Developing Web Feature Services to publish the metadata, and expose the locations, of pre-generated and virtual data rods in the GEOSS Portal and enable direct access of the data via Web services. SSW is also leveraged to increase the availability of both NASA and non-NASA data.3.Federating NASA Giovanni (Geospatial Interactive Online Visualization and Analysis Interface), for multi-sensor data exploration, that would allow each cooperating data center, currently the NASA Distributed Active Archive Centers (DAACs), to configure its own Giovanni deployment, while also allowing all the deployments to incorporate each others data. A federated Giovanni comprises Giovanni Virtual Machines, which can be run on local servers or in the cloud.

access↗

DOE OSTI2022

Development of an end state vision to implement digital monitoring in nuclear plants

Transitioning from an onsite Maintenance & Diagnostics Center to cloud-based services offers many new opportunities with computing power and storage, but also new challenges in terms of networking and security. This report will cover everything required for that transition including data processing and uploading to cloud services, feature selection, model creation, and result visualization for decision making. Although there are several other cloud-based services (e.g. Amazon Web Services and Google Cloud), this report explores Microsoft Azure to simplify nomenclature and maintain a consistent focus. Many of the services offered by Microsoft Azure are also available in the other cloud-based services, and their differences have been recorded in other literature. The Azure services most important to a nuclear power plant including networking & security, storage & databases, and Artificial Intelligence (AI) are reviewed here. Networking covers all aspects related to communication to Azure resources including security, privacy, and redundancy. Storage & databases includes data storage, upgrading, patching, backups, and monitoring. The AI services allows the user access to the machine learning (ML) techniques developed with Azure including automated ML, anomaly detection, computer vision, and natural language processing. This report summaries the features, capabilities, and challenges when using cloud-based services in a user-friendly manner.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRSDate not supplied

Changes in Characteristics of Future Climate Across the U.S.: Time Series Analysis of Climate Model Data by NASA POWER

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Data users can access these data either through an intuitive data viewer, image services fully integrable with GIS analysis, connections in the cloud through an Amazon Web Services S3 Bucket, or fully customizable access through an API. Data provided by POWER has been used to remotely monitor solar array fields and integrated in a sizing tool for off-grid solar and storage systems. POWER data has also been coupled with key building decision tools to support design and retrofitting of building energy systems for energy efficiency and reduction of greenhouse gases. POWER is now developing capabilities to provide time series of the projected future evolution of surface quantities important to future energy production and use, such as heating/cooling degree days, temperature, wind speed, and downwelling solar flux. We present here a range of possible future changes in these quantities at locations throughout the continental United States. We show how both average and extreme values of the quantities will evolve from present-day to future climate conditions. We plan to provide these projections for users in the energy and sustainable energy communities.

Bradley M. Hegyi↗

DOE OSTI2013

NATIONAL GEOTHERMAL DATA SYSTEM: AN EXEMPLAR OF OPEN ACCESS TO DATA

The formal launch of National Geothermal Data System (NGDS – www.geothermaldata.org) in 2014 will provide open access to technical geothermal-relevant data from all of the Department of Energy- sponsored geothermal development and research projects and geologic data from all 50 states. By making data easily discoverable and accessible this system will open new exploration opportunities and shorten project development. The prototype data system currently includes multiple data nodes, and nationwide data online and available to the public, indexed through a single catalog under construction at http://search.geothermaldata.org. Data from state geological surveys and partners includes more than 5 million records online, including 1.48 million well headers (oil and gas, water, geothermal), 732,000 well logs, and 314,000 borehole temperatures and is growing rapidly. There are over 250 Web services and another 138 WMS (Web Map Services) registered in the system as of August, 2013. Additional data record is being added by companion projects run by Boise State University, Southern Methodist University, and the USGS. The National Renewable Energy Laboratory is managing the Geothermal Data Repository, an NGDS node that will be a clearinghouse for data from hundreds of DOE-funded geothermal projects. NGDS is built on the US Geoscience Information Network (USGIN) data integration framework, which is a joint undertaking of the USGS and the Association of American State Geologists (AASG). NGDS is fully compliant with the White House Executive Order of May 2013, requiring all federal agencies to make their data holdings publicly accessible online in open source, interoperable formats with common core and extensible metadata. The National Geothermal Data System is being designed, built, deployed, and populated primarily with grants from the US Department of Energy, Geothermal Technologies Office. To keep this operational system sustainable after the original implementation will require four core elements: continued serving of data and applications by providers; maintenance of system operations; a governance structure; and an effective business model. Each of these presents a number of challenges currently under consideration.

15 GEOTHERMAL ENERGY↗

NASA NTRS2004

Grid Technology as a Cyberinfrastructure for Delivering High-End Services to the Earth and Space Science Community

Grid technology consists of middleware that permits distributed computations, data and sensors to be seamlessly integrated into a secure, single-sign-on processing environment. In &is environment, a user has to identify and authenticate himself once to the grid middleware, and then can utilize any of the distributed resources to which he has been,panted access. Grid technology allows resources that exist in enterprises that are under different administrative control to be securely integrated into a single processing environment The grid community has adopted commercial web services technology as a means for implementing persistent, re-usable grid services that sit on top of the basic distributed processing environment that grids provide. These grid services can then form building blocks for even more complex grid services. Each grid service is characterized using the Web Service Description Language, which provides a description of the interface and how other applications can access it. The emerging Semantic grid work seeks to associates sufficient semantic information with each grid service such that applications wii1 he able to automatically select, compose and if necessary substitute available equivalent services in order to assemble collections of services that are most appropriate for a particular application. Grid technology has been used to provide limited support to various Earth and space science applications. Looking to the future, this emerging grid service technology can provide a cyberinfrastructures for both the Earth and space science communities. Groups within these communities could transform those applications that have community-wide applicability into persistent grid services that are made widely available to their respective communities. In concert with grid-enabled data archives, users could easily create complex workflows that extract desired data from one or more archives and process it though an appropriate set of widely distributed grid services discovered using semantic grid technology. As required, high-end computational resources could be drawn from available grid resource pools. Using grid technology, this confluence of data, services and computational resources could easily be harnessed to transform data from many different sources into a desired product that is delivered to a user's workstation or to a web portal though which it could be accessed by its intended audience.

Hinke, Thomas H.↗

DOE OSTI2019

Scalable Reasoning System (SRS)

SRS is a web-based and mobile toolkit for information visualization and collaboration. It includes server-side components in the form of web services that provide analytical functions such as data clustering and temporal analysis. The SRS client is accessed via a web browser and uses asynchronous javascript and XML (AJAX) to interact with and visualize the results of these web services. Users of SRS can query data sources that are connected to the SRS backend, and query results are presented in the form of coordinated visual displays in a web browser that depict temporal, topical, and spatial relationships in the results. SRS also includes an early stage graphical reasoning diagram interface that helps analysts express and test hypotheses. This software was developed with funding from the Science and Technology Directorate of the U.S. Department of Homeland Security.

Pike, William↗

NASA NTRS2017

NASA Enterprise Managed Cloud Computing (EMCC): Delivering an Initial Operating Capability (IOC) for NASA use of Commercial Infrastructure-as-a-Service (IaaS)

In 2016, Ames supported the NASA CIO in delivering an initial operating capability for Agency use of commercial cloud computing. This presentation provides an overview of the project, the services approach followed, and the major components of the capability that was delivered. The presentation is being given at the request of Amazon Web Services to a contingent representing the Brazilian Federal Government and Defense Organization that is interested in the use of Amazon Web Services (AWS). NASA is currently a customer of AWS and delivered the Initial Operating Capability using AWS as its first commercial cloud provider. The IOC, however, designed to also support other cloud providers in the future.

IT Governance for Cloud Computing↗

NASA NTRS2011

Using Open and Interoperable Ways to Publish and Access LANCE AIRS Near-Real Time Data

The Atmospheric Infrared Sounder (AIRS) Near-Real Time (NRT) data from the Land Atmosphere Near real-time Capability for EOS (LANCE) element at the Goddard Earth Sciences Data and Information Services Center (GES DISC) provides information on the global and regional atmospheric state, with very low temporal latency, to support climate research and improve weather forecasting. An open and interoperable platform is useful to facilitate access to, and integration of, LANCE AIRS NRT data. As Web services technology has matured in recent years, a new scalable Service-Oriented Architecture (SOA) is emerging as the basic platform for distributed computing and large networks of interoperable applications. Following the provide-register-discover-consume SOA paradigm, this presentation discusses how to use open-source geospatial software components to build Web services for publishing and accessing AIRS NRT data, explore the metadata relevant to registering and discovering data and services in the catalogue systems, and implement a Web portal to facilitate users' consumption of the data and services.

Zhao, Peisheng↗

DOE OSTI2015

Cloud services for the Fermilab scientific stakeholders

As part of the Fermilab/KISTI cooperative research project, Fermilab has successfully run an experimental simulation workflow at scale on a federation of Amazon Web Services (AWS), FermiCloud, and local FermiGrid resources. We used the CernVM-FS (CVMFS) file system to deliver the application software. We established Squid caching servers in AWS as well, using the Shoal system to let each individual virtual machine find the closest squid server. We also developed an automatic virtual machine conversion system so that we could transition virtual machines made on FermiCloud to Amazon Web Services. We used this system to successfully run a cosmic ray simulation of the NOvA detector at Fermilab, making use of both AWS spot pricing and network bandwidth discounts to minimize the cost. On FermiCloud we also were able to run the workflow at the scale of 1000 virtual machines, using a private network routable inside of Fermilab. As a result, we present in detail the technological improvements that were used to make this work a reality.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

NASA POWER: Providing Present and Future Climate Services Based on NASA Data for the Energy, Agricultural, and Sustainable Buildings Communities

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the renewable energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Data users can access these data either through an intuitive data viewer, image services fully integrable with GIS analysis, connections in the cloud through an Amazon Web Services S3 Bucket, or fully customizable access through an API. Data provided by POWER has been successfully used by decision makers to support actions that address climate change. For example, POWER data has been used to remotely monitor solar array fields and integrated in a sizing tool for off-grid solar and storage systems. POWER data has also been coupled with key building decision tools to support design and retrofitting of building energy systems for energy efficiency and reduction of greenhouse gases. POWER is now developing climate services to provide time series of the projected future evolution of key quantities that interest our users, such as heating/cooling degree days, temperature, wind speed, and downwelling solar flux. We demonstrate the potential of the new climate services by presenting here a range of possible future changes in these quantities at different NASA centers across the continental United States. These data services are based on downscaled climate model data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data set. We highlight the important insights that new climate services can provide. Our climate services will help our user communities quantify the impacts of climate change to support their key decisions in planning for the future, both inside and outside the Federal Government, especially for decisions in renewable energy and in building heating and cooling.

Bradley Hegyi↗

DOE OSTI2015

A RESTful API for accessing microbial community data for MG-RAST

Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MGRAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http:// kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase’s microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

DAAC Collaboration Overview for AGU

The Atmospheric Science Data Center (ASDC), Goddard Earth Sciences Data and Information Systems Center (GES DISC), Socioeconomic Data and Applications Center (SEDAC), Oak Ridge National Laboratory (ORNL), Land Processes DAAC, Alaska Satellite Facility (ASF), as well as NASA Global Imagery Browse Service (GIBS), Earth Science Data Systems (ESDS) Geographic Information Systems Team (EGIST), Earthdata Content Delivery Team, ArcGIS Online Governance Team, and the Systematic Data Transformation ACCESS team have come together to establish the ArcGIS DAAC Collaboration. This coalition of participants will demonstrate their use of the ArcGIS Enterprise to support Earth science research, applied science, and outreach using Earth Observing System Data and Information System (EOSDIS) data. This includes the use of web services to fuse data products across space and time through services (e.g. ArcGIS Image Services, Open Geospatial Consortium (OGC) Web Coverage Service (WCS), and OGC Web Mapping Services (WMS)) for analysis in Jupyter notebooks, desktop tools, and web based applications.

Matthew Steven Tisdale↗

DOE OSTI2018

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

Background: According to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions. Objective: We propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities. Methods: We used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services. Results: We developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime. Conclusions: We introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2017

Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data Via Hyrax Server Data Server

This study explored three candidate architectures with different types of objects and access paths for serving NASA Earth Science HDF5 data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the study were: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and netCDF4 data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3). Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.We will describe the three architectures and the use cases along with performance results and recommendations for further work.

AWS cost↗