Search NASA⌕ Search

SEARCH · Search NASA

Results for “semantic data”

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.

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

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Monitoring Data-Structure Evolution in Distributed Message-Passing Programs

Monitoring the evolution of data structures in parallel and distributed programs, is critical for debugging its semantics and performance. However, the current state-of-art in tracking and presenting data-structure information on parallel and distributed environments is cumbersome and does not scale. In this paper we present a methodology that automatically tracks memory bindings (not the actual contents) of static and dynamic data-structures of message-passing C programs, using PVM. With the help of a number of examples we show that in addition to determining the impact of memory allocation overheads on program performance, graphical views can help in debugging the semantics of program execution. Scalable animations of virtual address bindings of source-level data-structures are used for debugging the semantics of parallel programs across all processors. In conjunction with light-weight core-files, this technique can be used to complement traditional debuggers on single processors. Detailed information (such as data-structure contents), on specific nodes, can be determined using traditional debuggers after the data structure evolution leading to the semantic error is observed graphically.

Sarukkai, Sekhar R.↗
DOE OSTI2014

Parallel processing of filtered queries in attributed semantic graphs

Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices ( semiring operations ). The user can also customize existing graph algorithms by writing filters. While the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Comprehensively, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Rapid detection of rare events from in situ X-ray diffraction data using machine learning

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ( e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Zheng, Weijian↗
DOE OSTI2022

Hod Carrier

SAND2023-06720O Hod Carrier is a simple proof-of-concept library for moving data from host to NVIDIA BlueField device memory using remote direct memory access. The Hod Carrier software also facilitates research activities related to the potential uses of NVIDIA BlueField data processing unit devices. Using well-established technologies (e.g., RDMA over Infiniband), it will move data from the host to the data processing unit’s memory. This software is a middleware utility for moving data without regard to its semantic meaning. Its operation is fundamentally unaffected by storage allocation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
NASA NTRS2002

An Extensible Schema-less Database Framework for Managing High-throughput Semi-Structured Documents

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword searches of records for both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semi-structured documents existing within NASA enterprises. Today, NETMARK is a flexible, high throughput open database framework for managing, storing, and searching unstructured or semi structured arbitrary hierarchal models, XML and HTML.

Maluf, David A.↗
NASA NTRS2003

An Extensible "SCHEMA-LESS" Database Framework for Managing High-Throughput Semi-Structured Documents

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semistructured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML.

Maluf, David A.↗
DOE OSTI2014

PAUSE: Predictive Analytics Using SPARQL-Endpoints

This invention relates to the medical industry and more specifically to methods of predicting risks. With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Sukumar, Sreenivas R↗
DOE OSTI2018

Data object classification using feature generation through crowdsourcing

In a computing device that implements a data object classification tool, a method for classifying data may include detecting change in spatial coordinates for each of at least two of a set of data objects within a canvas space. Each of the data objects may be associated with a vector of features. A rule set may be generated based on the vector of features associated with each of the at least two data objects. The rule set may use feature(s) that explain the changed spatial coordinates. The data objects may be selectively rearranged within the canvas space by applying the generated rule set to any remaining data objects among the set of data objects so as to assign spatial coordinates to the remaining objects. For each of the data objects, the spatial coordinates may be stored as new semantic feature(s) within the vector of features for that data object.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Are System Baselines within OT Environments Feasible?

Critical infrastructure stakeholders need to baseline their systems to understand expected protocol communications.Baseline behaviors may vary based on operational context.Expected operations during a maintenance window, for example, may be different from normal operations.Furthermore, constructing system baselines for Industrial Control Systems (ICS) is difficult and time-consuming.ICS processes generate artifacts expressed across heterogeneous data sources such as network and device logs. There needs to be a corpus of data in order to develop and compare methods that evaluate the feasibility, performance, and generality of approaches to construct baselines for ICS events. Standalone repositories of network packet captures are insufficient to develop methods to classify or recognize operational events expressed across multiple data sources. Moreover, static data corpora do not enable researchers to compare the impact of changing the underlying system for which a baseline is being constructed and this limits the ability to evaluate the performance of system baselines given system changes (e.g. patches, configuration, maintenance events). In order to address these limitations within the community, this talk intends to promote discussion about the state of the practice of constructing baselines. In this manner, we can continue to understand requirements within industry that are not being met by current approaches to baseline construction. This talk builds on two previous talks on the topic of system baselines for OT environments. First, Weaver co-presented at the RSA Conference ICS Sandbox with Dan Gunter. The talk confirmed the need within industry to construct baselines across multiple types of data sources relative to the semantics of specific business processes. Second, Weaver presented at IEEE Security and Privacy Workshop on Language-Theoretic Security.

02 PETROLEUM↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
NASA NTRS2010

Social Tagging of Mission Data

Mars missions will generate a large amount of data in various forms, such as daily plans, images, and scientific information. Often, there is a semantic linkage between images that cannot be captured automatically. Software is needed that will provide a method for creating arbitrary tags for this mission data so that items with a similar tag can be related to each other. The tags should be visible and searchable for all users. A new routine was written to offer a new and more flexible search option over previous applications. This software allows users of the MSLICE program to apply any number of arbitrary tags to a piece of mission data through a MSLICE search interface. The application of tags creates relationships between data that did not previously exist. These tags can be easily removed and changed, and contain enough flexibility to be specifically configured for any mission. This gives users the ability to quickly recall or draw attention to particular pieces of mission data, for example: Give a semantic and meaningful description to mission data; for example, tag all images with a rock in them with the tag "rock." Rapidly recall specific and useful pieces of data; for example, tag a plan as"driving template." Call specific data to a user s attention; for example, tag a plan as "for:User." This software is part of the MSLICE release, which was written in Java. It will run on any current Windows, Macintosh, or Linux system.

Norris, Jeffrey S.↗
NASA NTRSDate not supplied

Artificial Intelligence (AI) Methods for Augmenting the IMPACT Tool Evidence Library

Development of the Evidence Library for use with the IMPACT probability risk assessment tool took several years and involved a staggering amount of effort from a multi-disciplinary team. A very significant amount of the labor effort to collect, assess and finalize the Clinical Finding Form (CliFF) for each of the 119 medical conditions was provided by physician subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team. Many AI tools such as ChatGPT are excellent at summarizing large amounts of information and the current project was initiated to determine how such tools might streamline laborious processes, e.g., review and summarization of many scientific research publications, to execute key steps more efficiently in the process of developing CliFFs. The process for collecting the evidence which is found in the CliFFs is well documented in the Evidence Library Methods document (ELM; HRP-48036*). Using ELM and the CliFF development instructions as a guideline, a team of developers is leveraging Microsoft Azure AI tools and services along with open-source frameworks, to construct an AI-assisted automated pipeline. This pipeline is designed to search, retrieve, and process the necessary data sources, and ultimately help generate the final version of a CliFF. Currently, the large language model evaluates the relevance of each source material to spaceflights, either as direct evidence or as an analog. Additionally, the model assists in extracting keywords and generating brief summaries to enhance augmented retrieval and search processes in later stages of CliFF development. Once the data is ready, the model can perform semantic search and retrieval, generating and extracting valuable information for the CliFF. For instance, it can handle epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. The steps that required reading and summarizing articles were viewed as providing the greatest return on investment since large language models are very efficient and accurate in summarizing large amounts of text. Since labor effort to complete the original CliFF was not recorded with sufficient granularity, comparisons with an AI tool-generated CliFF will provide merely an approximation of time saved. Upon completion of the process, the CliFF for the medical condition “appendicitis” generated with the support of AI-based methods will serve as a proof-of-concept and will be compared to the original appendicitis CliFF to determine if use of the tools resulted in content and conclusory similarity. Based upon the results from face validation of the two CliFFs, modifications to the process will be made if necessary and additional condition CliFFs will be evaluated. Ultimately, CliFFs for the entire set of medical conditions will be created with the assistance of AI tools. Depending on the cost savings realized, CliFFs for additional medical conditions can be created to expand the Evidence Library. Future direction includes specifying the characteristics of the reviewer (prompting the AI tools to generate output assuming the reviewer is a sub-specialist physician, or nurse or EMT/medic) to determine if the effects on AI-generated output are different based on knowledge, skills and abilities. *Exploration Medical Capability Evidence Library Methods, HRP-48036 Rev A, July 2022.

Ali Al↗
NASA NTRS2011

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗
DOE OSTI2015

Resource Discovery for Extreme Scale Collaboration (RDESC) Final Report - RPI/TWC - Year 3

The amount of data produced in the practice of science is growing rapidly. Despite the accumulation and demand for scientific data, relatively little is actually made available for the broader scientific community. We surmise that the root of the problem is the perceived difficulty to electronically publish scientific data and associated metadata in a way that makes it discoverable. We propose to exploit Semantic Web technologies and practices to make (meta)data discoverable and easy to publish. We share our experiences in curating metadata to illustrate both the flexibility of our approach and the pain of discovering data in the current research environment. We also make recommendations by concrete example of how data publishers can provide their (meta)data by adding some limited, additional markup to HTML pages on the Web. With little additional effort from data publishers, the difficulty of data discovery/access/sharing can be greatly reduced and the impact of research data greatly enhanced.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2003

Organizing Diverse, Distributed Project Information

SemanticOrganizer is a software application designed to organize and integrate information generated within a distributed organization or as part of a project that involves multiple, geographically dispersed collaborators. SemanticOrganizer incorporates the capabilities of database storage, document sharing, hypermedia navigation, and semantic-interlinking into a system that can be customized to satisfy the specific information-management needs of different user communities. The program provides a centralized repository of information that is both secure and accessible to project collaborators via the World Wide Web. SemanticOrganizer's repository can be used to collect diverse information (including forms, documents, notes, data, spreadsheets, images, and sounds) from computers at collaborators work sites. The program organizes the information using a unique network-structured conceptual framework, wherein each node represents a data record that contains not only the original information but also metadata (in effect, standardized data that characterize the information). Links among nodes express semantic relationships among the data records. The program features a Web interface through which users enter, interlink, and/or search for information in the repository. By use of this repository, the collaborators have immediate access to the most recent project information, as well as to archived information. A key advantage to SemanticOrganizer is its ability to interlink information together in a natural fashion using customized terminology and concepts that are familiar to a user community.

Keller, Richard M.↗
DOE OSTI2018

Text-based Analytics for Biosurveillance

The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when). The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when).

Biomedical NLP↗
NASA NTRS2006

Adding Hierarchical Objects to Relational Database General-Purpose XML-Based Information Managements

NETMARK is a flexible, high-throughput software system for managing, storing, and rapid searching of unstructured and semi-structured documents. NETMARK transforms such documents from their original highly complex, constantly changing, heterogeneous data formats into well-structured, common data formats in using Hypertext Markup Language (HTML) and/or Extensible Markup Language (XML). The software implements an object-relational database system that combines the best practices of the relational model utilizing Structured Query Language (SQL) with those of the object-oriented, semantic database model for creating complex data. In particular, NETMARK takes advantage of the Oracle 8i object-relational database model using physical-address data types for very efficient keyword searches of records across both context and content. NETMARK also supports multiple international standards such as WEBDAV for drag-and-drop file management and SOAP for integrated information management using Web services. The document-organization and -searching capabilities afforded by NETMARK are likely to make this software attractive for use in disciplines as diverse as science, auditing, and law enforcement.

Lin, Shu-Chun↗