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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Resilient data staging through MxN distributed transactions.

Scientific computing-driven discoveries are frequently driven from workflows that use persistent storage as a staging area for data between operations. With the bad and progressively worse bandwidth vs. data size issues as we continue towards exascale, eliminating persistent storage through techniques like data staging will both enable these workflows to continue online, but also enable more interactive workflows reducing the time to scientific discoveries. Data staging has shown to be an effective way for applications running on high-end computing platforms to offload expensive I/O operations and to manage the tremendous amounts of data they produce. This data staging approach, however, lacks the ACID style guarantees traditional straight-to-disk methods provide. Distributed transactions are a proven way to add ACID properties to data movements, however distributed transactions follow 1xN data movement semantics, where our highly parallel HPC environments employ MxN data movement semantics. In this paper we present a novel protocol that extends distributed transaction terminology to include MxN semantics which allows our data staging areas to benefit from ACID properties. We show that with our protocol we can provide resilient data staging with a limited performance penalty over current data staging implementations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

First Application of Geospatial Semantic Graphs to SAR Image Data (LDRD Final Report)

Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report a preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.

Brost, Randolph C.↗
DOE OSTI2012

Relational Blackboard

The Relational Blackboard (RBB) is an extension of the H2 Relational Database to support discrete events and timeseries data. The original motivation for RBB is as a knowledge base for cognitive systems and simulations. It is useful wherever there is a need for persistent storage of timeseries (i.e. samples of a continuous process generating numerical data) and semantic labels for the data. The RBB is an extension to the H2 Relational Database, which is open-source. RBB is a set of stored procedures for H2 allowing data to be labeled, queried, and resampled.

Abbott, RobertG↗
DOE OSTI2012

Heterogeneous Data Fusion via Space Alignment Using Nonmetric Multidimensional Scaling

Heterogeneous data sets are typically represented in different feature spaces, making it difficult to analyze relationships spanning different data sets even when they are semantically related. Data fusion via space alignment can remedy this task by integrating multiple data sets lying in different spaces into one common space. Given a set of reference correspondence data that share the same semantic meaning across different spaces, space alignment attempts to place the corresponding reference data as close together as possible, and accordingly, the entire data are aligned in a common space. Space alignment involves optimizing two potentially conflicting criteria: minimum deformation of the original relationships and maximum alignment between the different spaces. To solve this problem, we provide a novel graph embedding framework for space alignment, which converts each data set into a graph and assigns zero distance between reference correspondence pairs resulting in a single graph. We propose a graph embedding method for fusion based on nonmetric multidimensional scaling (MDS). Its criteria using the rank order rather than the distance allows nonmetric MDS to effectively handle both deformation and alignment. Experiments using parallel data sets demonstrate that our approach works well in comparison to existing methods such as constrained Laplacian eigenmaps, Procrustes analysis, and tensor decomposition. We also present standard cross-domain information retrieval tests as well as interesting visualization examples using space alignment.

Alignment↗
DOE OSTI2011

Massive-scale RDF Processing Using Compressed Bitmap Indexes

The Resource Description Framework (RDF) is a popular data model for representing linked data sets arising from the web, as well as large scienti c data repositories such as UniProt. RDF data intrinsically represents a labeled and directed multi-graph. SPARQL is a query language for RDF that expresses subgraph pattern- nding queries on this implicit multigraph in a SQL- like syntax. SPARQL queries generate complex intermediate join queries; to compute these joins e ciently, we propose a new strategy based on bitmap indexes. We store the RDF data in column-oriented structures as compressed bitmaps along with two dictionaries. This paper makes three new contributions. (i) We present an e cient parallel strategy for parsing the raw RDF data, building dictionaries of unique entities, and creating compressed bitmap indexes of the data. (ii) We utilize the constructed bitmap indexes to e ciently answer SPARQL queries, simplifying the join evaluations. (iii) To quantify the performance impact of using bitmap indexes, we compare our approach to the state-of-the-art triple-store RDF-3X. We nd that our bitmap index-based approach to answering queries is up to an order of magnitude faster for a variety of SPARQL queries, on gigascale RDF data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Precedency control and other semantic integrity issues in a workbench database

Most database systems model the current state of a system of real world discrete and simple entities together with their relationships. By examining instead a database system that is a workbench and models more complicated entities, a fresh perspective is gained. Specifically, semantic integrity is analysed. Four aspects distinct from physical integrity are identified, namely - access, failure, concurrency and precedency. Access control is shown to be the consequence of semantic interdependency between data and its matching semantic routines. Failure, concurrency precedency controls are concerned with preventing processes interfering with each other. Precedency is a new concept in the database context. It expresses a constraint between processes that act on the database. As processes create, update and delete entities they in general obey a partial ordering imposed by the semantics of their actions. Precedency control ensures that data remains consistent with respect to this partial order.

Dampney, C. N. G.↗
DOE OSTI2015

EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'

Source record↗
DOE OSTI2014

Semantic search integration to climate data

In this paper we present how research projects at Oak Ridge National Laboratory are using Semantic Search capabilities to help scientists perform their research. We will discuss how the Mercury metadata search system, with the help of the semantic search capability, is being used to find, retrieve, and link climate change data. DOI: 10.1109/CTS.2014.6867639

Devarakonda, Ranjeet↗
DOE OSTI2016

Semantic Catalog of Things, Services, and Data to Support a Wind Data Management Facility

The purpose of this paper is to discuss how community vocabularies and linked open data best practices are being used to seamlessly link things, data, and off the shelf services to support scientific offshore wind energy research for the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) Wind and Water Power Program. This is largely made possible by leveraging collaborative advances in the Internet of Things (IoT), Semantic Web, Linked Services, Linked Open Data (LOD), and RDF vocabulary communities, which provide the foundation for our design. By adapting these linked community best practices, we designed a wind characterization data management facility capable of continually collecting, processing, and preservation of in situ and remote sensing instrume

atmosphere↗
NASA NTRSDate not supplied

TPSAS-NF1676L-16833-DND

Semantic Infrastructure is central to realizing the first goal of the ASDC's Strategic Plan: expanding the ASDC's customer base by improving access to ASDC data. ASDC data comprises a widely heterogeneous set of complex products which presents two significant challenges in data access: Helping customers discover, among many available options, the most suitable data products for their purpose; and Guiding customers to easily and appropriately use products. Data products differ significantly in terms of how the data was collected and processed, even with similar subject matter. Understanding differences is critical to using data effectively. To reach a broader customer range, the ASDC must provide prospective users with enough information to quickly and meaningfully compare and evaluate data products. Data formats and structures also differ among products. Applications displaying and analyzing data need access to federated and semantically disambiguated data. Semantic technologies offer functionality for addressing this issue. Ontologies can provide robust, stable domain models serving as common schema for discovering, evaluating, comparing, and integrating data from disparate products. Reasoning engines and triple stores can leverage ontologies to support intelligent search applications allowing users to discover, query, retrieve, and easily reformat data from a broad spectrum of sources.

Beth Huffer↗
DOE OSTI2014

Scaling Irregular Applications through Data Aggregation and Software Multithreading

Bioinformatics, data analytics, semantic databases, knowledge discovery are emerging high performance application areas that exploit dynamic, linked data structures such as graphs, unbalanced trees or unstructured grids. These data structures usually are very large, requiring significantly more memory than available on single shared memory systems. Additionally, these data structures are difficult to partition on distributed memory systems. They also present poor spatial and temporal locality, thus generating unpredictable memory and network accesses. The Partitioned Global Address Space (PGAS) programming model seems suitable for these applications, because it allows using a shared memory abstraction across distributed-memory clusters. However, current PGAS languages and libraries are built to target regular remote data accesses and block transfers. Furthermore, they usually rely on the Single Program Multiple Data (SPMD) parallel control model, which is not well suited to the fine grained, dynamic and unbalanced parallelism of irregular applications. In this paper we present {\bf GMT} (Global Memory and Threading library), a custom runtime library that enables efficient execution of irregular applications on commodity clusters. GMT integrates a PGAS data substrate with simple fork/join parallelism and provides automatic load balancing on a per node basis. It implements multi-level aggregation and lightweight multithreading to maximize memory and network bandwidth with fine-grained data accesses and tolerate long data access latencies. A key innovation in the GMT runtime is its thread specialization (workers, helpers and communication threads) that realize the overall functionality. We compare our approach with other PGAS models, such as UPC running using GASNet, and hand-optimized MPI code on a set of typical large-scale irregular applications, demonstrating speedups of an order of magnitude.

Aggregation↗
DOE OSTI2011

Combinatorial Information Theoretical Measurement of the Semantic Significance of Semantic Graph Motifs

Given an arbitrary semantic graph data set, perhaps one lacking in explicit ontological information, we wish to first identify its significant semantic structures, and then measure the extent of their significance. Casting a semantic graph dataset as an edge-labeled, directed graph, this task can be built on the ability to mine frequent {\em labeled} subgraphs in edge-labeled, directed graphs. We begin by considering the fundamentals of the enumerative combinatorics of subgraph motif structures in edge-labeled directed graphs. We identify its frequent labeled, directed subgraph motif patterns, and measure the significance of the resulting motifs by the information gain relative to the expected value of the motif based on the empirical frequency distribution of the link types which compose them, assuming indpendence. We illustrate the method on a small test graph, and discuss results obtained for small linear motifs (link type bigrams and trigrams) in a larger graph structure.

Joslyn, Cliff A.↗
DOE OSTI2013

From data to analysis: linking NWChem and Avogadro with the syntax and semantics of Chemical Markup Language

Background Multidisciplinary integrated research requires the ability to couple the diverse sets of data obtained from a range of complex experiments and computer simulations. Integrating data requires semantically rich information. In this paper the generation of semantically rich data from the NWChem computational chemistry software is discussed within the Chemical Markup Language (CML) framework. Results The NWChem computational chemistry software has been modified and coupled to the FoX library to write CML compliant XML data files. The FoX library was expanded to represent the lexical input files used by the computational chemistry software. Conclusions The production of CML compliant XML files for the computational chemistry software NWChem can be relatively easily accomplished using the FoX library. A unified computational chemistry or CompChem convention and dictionary needs to be developed through a community-based effort. The long-term goal is to enable a researcher to do Google-style chemistry and physics searches.

Chemical Markup Language, FoX, NWChem, computation↗
NASA NTRSDate not supplied

Transformation of the NASA Life Sciences Portal to a FAIR Data Point

The FAIR principles emphasize optimizing metadata, the vast majority of which are textual in nature, and often organized into attribute name-value pairs. This uniformity has led to the development of guidelines and best practices for providing programmatic access to scientific data through their metadata, yielding the first iteration of the FAIR Data Point Specifications (FDPS). A key feature of the FDPS is its support for automated agents seeking and fetching data without first needing to learn a plethora of different application programming interfaces. These software agents can interrogate metadata catalogs that adhere to FDPS in a uniform manner because each catalog describes itself and its metadata schema consistently. This approach enhances the sustainability of data retrieval support, allowing systems to refine and update their metadata schemas as needed and without requiring data-seeking software agents to change how they interrogate FDPS catalogs. An essential aspect of the FDPS is the standardization of data catalog semantics, which formalizes concepts such as “metadata” and “metadata service” and links them to other concepts specifications including the Data Catalog Vocabulary (DCAT), a W3C standard that is also the basis of NASA-STD-2831 “Metadata Standard for Data Discoverability,” authored by NASA’s Office of the Chief Information Officer. The FDPS references DCAT (version 2) elements which focus on the distribution of datasets and support the goal of stream-lined catalog integration across repositories for improved data discovery. Additionally, the FDPS also prescribe the use of Linked Data Platform elements for data catalog-metadata record containment descriptions, allowing users to ascertain which data and metadata belong to which catalogs. NASA’s Life Sciences Portal is implementing the FDPS while formalizing its metadata schema to support the accelerated synthesis of knowledge from space life sciences investigations.

platform↗