Search NASA⌕ Search

SEARCH · Search NASA

Results for “scientific Data Management”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Scientific Data Management Center for Enabling Technologies

Managing scientific data has been identified by the scientific community as one of the most important emerging needs because of the sheer volume and increasing complexity of data being collected. Effectively generating, managing, and analyzing this information requires a comprehensive, end-to-end approach to data management that encompasses all of the stages from the initial data acquisition to the final analysis of the data. Fortunately, the data management problems encountered by most scientific domains are common enough to be addressed through shared technology solutions. Based on community input, we have identified three significant requirements. First, more efficient access to storage systems is needed. In particular, parallel file system and I/O system improvements are needed to write and read large volumes of data without slowing a simulation, analysis, or visualization engine.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Scientific Data Management Beyond Traditional Computing Boundaries

Scientific data management is undergoing a fundamental transformation driven by the convergence of artificial intelligence (AI)/machine learning workflows, distributed computing and storage environments, and exponential data growth. Here, we analyze how these developments address current limitations while enabling new capabilities for cross-facility collaboration and AI-driven research.

Widener, Patrick [Oak Ridge National Laboratory (O↗
NASA NTRS1989

Adding intelligence to scientific data management

NASA plans to solve some of the problems of handling large-scale scientific data bases by turning to artificial intelligence (AI) are discussed. The growth of the information glut and the ways that AI can help alleviate the resulting problems are reviewed. The employment of the Intelligent User Interface prototype, where the user will generate his own natural language query with the assistance of the system, is examined. Spatial data management, scientific data visualization, and data fusion are discussed.

Campbell, William J.↗
NASA NTRS2001

XML Based Scientific Data Management Facility

The World Wide Web consortium has developed an Extensible Markup Language (XML) to support the building of better information management infrastructures. The scientific computing community realizing the benefits of HTML has designed markup languages for scientific data. In this paper, we propose a XML based scientific data management facility, XDMF. The project is motivated by the fact that even though a lot of scientific data is being generated, it is not being shared because of lack of standards and infrastructure support for discovering and transforming the data. The proposed data management facility can be used to discover the scientific data itself, the transformation functions, and also for applying the required transformations. We have built a prototype system of the proposed data management facility that can work on different platforms. We have implemented the system using Java, and Apache XSLT engine Xalan. To support remote data and transformation functions, we had to extend the XSLT specification and the Xalan package.

Mehrotra, Piyush↗
NASA NTRS2002

XML Based Scientific Data Management Facility

The World Wide Web consortium has developed an Extensible Markup Language (XML) to support the building of better information management infrastructures. The scientific computing community realizing the benefits of XML has designed markup languages for scientific data. In this paper, we propose a XML based scientific data management ,facility, XDMF. The project is motivated by the fact that even though a lot of scientific data is being generated, it is not being shared because of lack of standards and infrastructure support for discovering and transforming the data. The proposed data management facility can be used to discover the scientific data itself, the transformation functions, and also for applying the required transformations. We have built a prototype system of the proposed data management facility that can work on different platforms. We have implemented the system using Java, and Apache XSLT engine Xalan. To support remote data and transformation functions, we had to extend the XSLT specification and the Xalan package.

Mehrotra, P.↗
NASA NTRS1984

CAD/CAM and scientific data management at Dassault

The history of CAD/CAM and scientific data management at Dassault are presented. Emphasis is put on the targets of the now commercially available software CATIA. The links with scientific computations such as aerodynamics and structural analysis are presented. Comments are made on the principles followed within the company. The consequences of the approximative nature of scientific data are examined. Consequence of the new history function is mainly its protection against copy or alteration. Future plans at Dassault for scientific data appear to be in opposite directions compared to some general tendencies.

Bohn, P.↗
DOE OSTI2012

Scientific Data Management Integrated Software Infrastructure Center (SDM/ISIC): Scientific Process Automation (SPA) (Final Report)

The KEPLER project has made a significant impact and established a sustained leadership in the field of scientific workflows. The open source KEPLER project started in 2003 when members of the SciDAC-SDM Center/SPA Team, sponsored by this DOE-funded project (DE-FC02-01ER25486) and members of the SEEK project (NSF/ITR awards DBI-0225674 and DBI-0533368) decided to collaborate and jointly develop a scientific workflow system based on the open source PTOLEMY II system (Ludäscher was a co-PI on both projects). In the first project phase, between 2001 and 2003, the SDSC team (Altintas, Ludäscher) worked closely with a domain scientist (Matt Coleman, LLNL) and created early versions of a Promoter Identification Workflow (PIW). Towards the end of that period, the open source PTOLEMY II system was adopted by SciDAC-SDM and SEEK as the basis for a general scientific workflow system and problem-solving environment to design and execute scientific workflows, giving rise to Kepler. Once started through SciDAC-SDM and SEEK, the KEPLER leadership team was able to attract further support from funding agencies including from DOE (as part of SciDAC-2), NSF, NIH, and the Gordon and Betty Moore Foundation. Important advances to KEPLER were made by the SciDAC/SDM-SPA team during the report period (2001–2007), in particular by the SDSC team (and after Ludäscher's move) the UC Davis team, including (i) research and development of the workflow infrastructure, (ii) library (actor) development, and (iii) development of concrete scientific workflows, in collaboration with scientists. Years later, the KEPLER system was listed as one of the prominent “Big Data” research outcomes of DOE in the White House Big Data Factsheet, announced as part of the White House Big Data press release.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Exacution: Enhancing Scientific Data Management for Exascale

As we continue toward exascale, scientific data volume is continuing to scale and becoming more burdensome to manage. In this paper, we lay out opportunities to enhance state of the art data management techniques. We emphasize well-principled data compression, and using it to achieve progressive refinement. This can both accelerate I/O and afford the user increased flexibility when she interacts with the data. The formulation naturally maps onto enabling partitioning of the progressively improving-quality representations of a data quantity into different media-type destinations, to keep the highest priority information as close as possible to the computation, and take advantage of deepening memory/storage hierarchies in ways not previously possible. Careful monitoring is requisite to our vision, not only to verify that compression has not eliminated salient features in the data, but also to better understand the performance of massively parallel scientific applications. Increased mathematical rigor would be ideal,to help bring compression on a better-understood theoretical footing, closer to the relevant scientific theory, more aware of constraints imposed by the science, and more tightly error-controlled. Throughout, we highlight pathfinding research we have begun exploring related these topics, and comment toward future work that will be needed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Rucio: Scientific Data Management

Rucio is an open-source software framework that provides scientific collaborations with the functionality to organize, manage, and access their data at scale. The data can be distributed across heterogeneous data centers at widely distributed locations. Rucio was originally developed to meet the requirements of the high-energy physics experiment ATLAS, and now is continuously extended to support the LHC experiments and other diverse scientific communities. In this article, we detail the fundamental concepts of Rucio, describe the architecture along with implementation details, and report operational experience from production usage.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1978

SDMS: A scientific data management system

SDMS is a data base management system developed specifically to support scientific programming applications. It consists of a data definition program to define the forms of data bases, and FORTRAN-compatible subroutine calls to create and access data within them. Each SDMS data base contains one or more data sets. A data set has the form of a relation. Each column of a data set is defined to be either a key or data element. Key elements must be scalar. Data elements may also be vectors or matrices. The data elements in each row of the relation form an element set. SDMS permits direct storage and retrieval of an element set by specifying the corresponding key element values. To support the scientific environment, SDMS allows the dynamic creation of data bases via subroutine calls. It also allows intermediate or scratch data to be stored in temporary data bases which vanish at job end.

Massena, W. A.↗
DOE OSTI2013

Scientific Data Management (SDM) Center for Enabling Technologies. 2007-2012

Over the past five years, our activities have both established Kepler as a viable scientific workflow environment and demonstrated its value across multiple science applications. We have published numerous peer-reviewed papers on the technologies highlighted in this short paper and have given Kepler tutorials at SC06,SC07,SC08,and SciDAC 2007. Our outreach activities have allowed scientists to learn best practices and better utilize Kepler to address their individual workflow problems. Our contributions to advancing the state-of-the-art in scientific workflows have focused on the following areas. Progress in each of these areas is described in subsequent sections. Workflow development. The development of a deeper understanding of scientific workflows "in the wild" and of the requirements for support tools that allow easy construction of complex scientific workflows; Generic workflow components and templates. The development of generic actors (i.e.workflow components and processes) which can be broadly applied to scientific problems; Provenance collection and analysis. The design of a flexible provenance collection and analysis infrastructure within the workflow environment; and, Workflow reliability and fault tolerance. The improvement of the reliability and fault-tolerance of workflow environments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

A Conceptual Enterprise Framework for Managing Scientific Data Stewardship

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

Scientific data stewardship↗
DOE OSTI2016

Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessible

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are known as user facilities. They are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Pilot climate data system: A state-of-the-art capability in scientific data management

The Pilot Climate Data System (PCDS) was developed by the Information Management Branch of NASA's Goddard Space Flight Center to manage a large collection of climate-related data of interest to the research community. The PCDS now provides uniform data catalogs, inventories, access methods, graphical displays and statistical calculations for selected NASA and non-NASA data sets. Data manipulation capabilities were developed to permit researchers to easily combine or compare data. The current capabilities of the PCDS include many tools for the statistical survey of climate data. A climate researcher can examine any data set of interest via flexible utilities to create a variety of two- and three-dimensional displays, including vector plots, scatter diagrams, histograms, contour plots, surface diagrams and pseudo-color images. The graphics and statistics subsystems employ an intermediate data storage format which is data-set independent. Outside of the graphics system there exist other utilities to select, filter, list, compress, and calculate time-averages and variances for any data of interest. The PCDS now fully supports approximately twenty different data sets and is being used on a trial basis by several different in-house research grounds.

Smith, P. H.↗
NASA NTRS2012

Frameworks Coordinate Scientific Data Management

Jet Propulsion Laboratory computer scientists developed a unique software framework to help NASA manage its massive amounts of science data. Through a partnership with the Apache Software Foundation of Forest Hill, Maryland, the technology is now available as an open-source solution and is in use by cancer researchers and pediatric hospitals.

Source record↗
NASA NTRS2013

Towards Efficient Scientific Data Management Using Cloud Storage

A software prototype allows users to backup and restore data to/from both public and private cloud storage such as Amazon's S3 and NASA's Nebula. Unlike other off-the-shelf tools, this software ensures user data security in the cloud (through encryption), and minimizes users operating costs by using space- and bandwidth-efficient compression and incremental backup. Parallel data processing utilities have also been developed by using massively scalable cloud computing in conjunction with cloud storage. One of the innovations in this software is using modified open source components to work with a private cloud like NASA Nebula. Another innovation is porting the complex backup to- cloud software to embedded Linux, running on the home networking devices, in order to benefit more users.

He, Qiming↗