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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Nuclear Data Management and Analysis System Plan

The United States Department of Energy Advanced Reactor Technologies Program was formed in Fiscal Year 2015 and encompasses the Next Generation Nuclear Plant Project and Very High Temperature Reactor (VHTR) Program as they were known previously. The VHTR Program was created to support design and licensing of the first VHTR nuclear plant. Data created for and used by the program must be qualified for use, stored in a readily accessible electronic form, categorized to assure the correct data are used, and controlled to prevent data corruption or inadvertent changes. The Nuclear Data Management and Analysis System was designed to support the data needs of the VHTR Program, at the time and now the Advanced Reactor Technologies Program. Since its inception, use of the Nuclear Data Management and Analysis System has expanded to support additional projects and programs with similar requirements for control, analysis, and availability of large data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Data Management at the Advanced Photon Source

Data are crucial to the scientific discoveries enabled by experiments performed at the APS. Presently the APS generates approximately 10 PB of raw experimental data per year from its sixty-six operating beamlines that house over 100 unique instruments. This data is generated as a part of over 6,000 annual experiments performed by over 5,500 facility users each year. Similar to other synchrotrons, the amount of data generated at the APS continues to quickly increase due to beamline advances, such as new measurement techniques, technological advances in detectors, high-throughput instrumentation, multi-modal instruments that can acquire several measurements in a single experiment, and advanced data processing algorithms. This trend is expected to continue in the future. The revolutionized high-energy synchrotron facility proposed in the APS-U project will increase brightness and coherence, leading to further increases in data rates. Successful management of data is of vital importance to the current and future scientific productivity of the APS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1993

DataHub - Science data management in support of interactive exploratory analysis

DataHub addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactive nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc.) in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis is on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗
DOE OSTI2017

Energy Data Management Manual for the Wastewater Treatment Sector

Energy efficiency has become a higher priority within the wastewater treatment sector, with facility operators and state and local governments ramping up efforts to reduce energy costs and improve environmental performance. Across the country, municipal wastewater treatment plants are estimated to consume more than 30 terawatt hours per year of electricity, which equates to about $2 billion in annual electric costs. Electricity alone can constitute 25% to 40% of a wastewater treatment plant’s annual operating budget and make up a significant portion of a given municipality’s total energy bill. These energy needs are expected to grow over time, driven by population growth and increasingly stringent water quality requirements. The purpose of this document is to describe the benefits of energy data management, explain how it can help drive savings when linked to a strong energy management program, and provide clear, step-by-step guidance to wastewater treatment plants on how to appropriately track energy performance. It covers the basics of energy data management and related concepts and describes different options for key steps, recognizing that a single approach may not work for all agencies. Wherever possible, the document calls out simpler, less time-intensive approaches to help smaller plants with more limited resources measure and track energy performance. Reviews of key, publicly available energy-tracking tools are provided to help organizations select a tool that makes the most sense for them. Finally, this document describes additional steps wastewater treatment plant operators can take to build on their energy data management systems and further accelerate energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Transportation Storage Logistics (TSL) Model Data Management Manual

The Transportation Storage Logistics (TSL) Model Data Management Manual describes the SQL Server database that is used by the TSL model to house reference data as well as the scenario data. The manual is a reference document, subject to change as the model evolves.

Busch, Ingrid Karin↗
NASA NTRS1993

DataHub: Science data management in support of interactive exploratory analysis

The DataHub addresses four areas of significant needs: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactives nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc), in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗
NASA NTRS1993

A parallel data management system for large-scale NASA datasets

The past decade has experienced a phenomenal growth in the amount of data and resultant information generated by NASA's operations and research projects. A key application is the reprocessing problem which has been identified to require data management capabilities beyond those available today (PRAT93). The Intelligent Information Fusion (IIF) system (ROEL91) is an ongoing NASA project which has similar requirements. Deriving our understanding of NASA's future data management needs based on the above, this paper describes an approach to using parallel computer systems (processor and I/O architectures) to develop an efficient parallel database management system to address the needs. Specifically, we propose to investigate issues in low-level record organizations and management, complex query processing, and query compilation and scheduling.

Srivastava, Jaideep↗
NASA NTRS2019

Evaluation of Technology Concepts for Traffic Data Management and Relevant Audio for Datalink in Commercial Airline Flight Decks

Datalink is currently operational for departure clearances and in oceanic environments and is currently being tested in high altitude domestic enroute airspace. Interaction with even simple datalink clearances may create more workload for flight crews than the voice system they replace if not carefully designed. Datalink may also introduce additional complexity for flight crews with hundreds of uplink messages now defined for use. Finally, flight crews may lose airspace awareness and operationally relevant information that they normally pickup from Air Traffic Control (ATC) voice communications with other aircraft (i.e., “party-line” transmissions). Once again, automation may be poised to increase workload on the flight deck for incremental benefit. Datalink implementation to support future air traffic management concepts needs to be carefully considered, understanding human communication norms and especially, the change from voice- to text-based communications modality and its effect on pilot workload and situation awareness. Increasingly autonomous systems, where autonomy is designed to support human-autonomy teaming, may be suited to solve these issues. NASA is conducting research and development of increasingly autonomous systems, utilizing machine-learning algorithms seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. Increasingly autonomous systems offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. Two increasingly autonomous systems concepts - a traffic data manager and a conversational co-pilot - were developed to intelligently address the datalink issues in a complex, future state environment with significant levels of traffic. The system was tested for suitability of datalink usage for terminal airspace. The traffic data manager allowed for automated declutter of the Automatic Dependent Surveillance-Broadcast (ADS-B) display. The system determined relevant traffic for display based on machine learning algorithms trained by experienced human pilot behaviors. The conversational co-pilot provided relevant audio air traffic control messages based on context and proximity to ownship. Both systems made use of the connected aircraft concepts to provide intelligent context to determine relevancy above and beyond proximity to ownship. A human-in-the-loop test was conducted in NASA Langley Research Center’s Integration Flight Deck B-737-800 simulator to evaluate the traffic data manager and the conversational co-pilot. Twelve airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to the procedural events. This paper details the flight crew performance and evaluation during the events.

Etherington, Timothy↗
DOE OSTI2018

A Conditions Data Management System for HEP Experiments

Conditions data infrastructure for both ATLAS and CMS have to deal with the management of several Terabytes of data. Distributed computing access to this data requires particular care and attention to manage request-rates of up to several tens of kHz. Thanks to the large overlap in use cases and requirements, ATLAS and CMS have worked towards a common solution for conditions data management with the aim of using this design for data-taking in Run 3. In the meantime other experiments, including NA62, have expressed an interest in this cross- experiment initiative. For experiments with a smaller payload volume and complexity, there is particular interest in simplifying the payload storage. The conditions data management model is implemented in a small set of relational database tables. A prototype access toolkit consisting of an intermediate web server has been implemented, using standard technologies available in the Java community. Access is provided through a set of REST services for which the API has been described in a generic way using standard Open API specications, implemented in Swagger. Such a solution allows the automatic generation of client code and server stubs and further allows changes in the backend technology transparently. An important advantage of using a REST API for conditions access is the possibility of caching identical URLs, addressing one of the biggest challenges that large distributed computing solutions impose on conditions data access, avoiding direct DB access by means of standard web proxy solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

High-performance data management for whole slide image analysis in digital pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Wang, Xiao↗
DOE OSTI2024

LSE-61: Rubin Observatory Data Management System (DMS) Requirements

This document contains the top-level requirements for the Data Management subsystem of the LSST, when combined with the Data Management-related requirements in the LSST System Requirements (LSR, LSE-29) and Observatory System Specifications (OSS, LSE-30).

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1988

The Space Station Data Management System - Avionics that integrate

The Space Station Data Management System (DMS) comprises the networked computers, mass storage, workstations, and instrumentation interfaces required to support onboard systems and payload operations. This paper gives an overview of the current DMS architecture and discusses its role as onboard integrator in four of its major functional areas: (1) data communication; (2) data processing; (3) data administration, storage and retrieval; and (4) data presentation at the human-computer interface.

Whitelaw, Virginia↗
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 OSTI2023

Assessment of Data-Management Infrastructure Needs for Production Use of Advanced Machine Learning and Artificial Intelligence: Tri-Lab Level II Milestone (8554)

A robust data-management infrastructure is a key enabler for National Security Enterprise (NSE) capabilities in artificial intelligence and machine learning. This document describes efforts from a team of researchers at Sandia National Laboratories, Los Alamos National Laboratory, and Livermore National Laboratory to complete ASC Level II milestone #8854 “Assessment of Data-Management Infrastructure Needs for Production use of Advanced Machine learning and Artificial Intelligence.”

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

MERIT: A man/computer data management and enhancement system for upper air nowcasting/forecasting in the United States

It is suggested that the very short range forecast problem for aviation is one of data management rather than model development and the possibility of improving the aviation forecast using current technology is underlined. The MERIT concept of modeling technology, and advanced man/computer interactive data management and enhancement techniques to provide a tailored, accurate and timely forecast for aviation is outlined. The MERIT includes utilization of the Langrangian approach, extensive use of the automated aircraft report to complement the present data base and provide the most current observations; and the concept that a 2 to 12 hour forecast provided every 3 hr can meet the domestic needs of aviation instead of the present 18 and 24 hr forecast provided every 12 hr.

Steinberg, R.↗
NASA NTRS1991

Overview of the System of Experts for Intelligent Data Management (SEIDAM)

Viewgraphs on the system of experts for intelligent data management (SEIDAM) are presented. Topics covered include: multiple data sources integration; SEIDAM structure; machine learning; user interface; spatial knowledge expert system; digital terrain model (DTM) and segmentation expert systems; system validation; and tasks and plans.

Goodenough, David G.↗
DOE OSTI2019

FUSRAP Data Management and Information Transitions - 19459

The Formerly Utilized Sites Remedial Action Program (FUSRAP) was created in the mid-1970's to clean up radiological contamination resulting from early development of nuclear weapons. The US Department of Energy (US DOE) was responsible for FUSRAP until October 1997, when Congress transferred administration and execution of FUSRAP site cleanup to the US Army Corps of Engineers (USACE). Currently, the US DOE Office of Legacy Management (LM) determines the eligibility of a site for referral to USACE. USACE determines whether the site is included in FUSRAP for cleanup, conducts remedial actions, and transfers sites back to LM for long-term surveillance and maintenance (LTS and M). LM and USACE established the Data Management Working Group (DMWG) to (1) establish a common understanding of US DOE and USACE district-specific data, records practices, and requirements as they apply to FUSRAP programmatic documents for site transition and transfer; (2) establish methods to ensure accurate data transfer; and (3) develop a timeline for data and records transfer. These methods will ensure continuity of stewardship while transitioning from cleanup to LTS and M and beyond as more FUSRAP sites transfer from USACE to LM. The DMWG has met monthly and the meetings are attended by a wide range of LM and USACE project managers and subject matter experts. Discussion topics have addressed site records management, including data needs and uses; transfer and posting of Administrative Record and Permanent Record files for public accessibility; and management of legal and classified files. There have been a number of group successes, including the following: - Establishment of a common understanding of data and records practices and requirements as the sites transition and transfer from USACE to LM; - Development of effective lines of communication between USACE and LM concerning data management for transition; - Establishment of a cohesive approach to data management between the two agencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1987

Space Station data management system architecture

Within the Space Station program, the Data Management System (DMS) functions in a dual role. First, it provides the hardware resources and software services which support the data processing, data communications, and data storage functions of the onboard subsystems and payloads. Second, it functions as an integrating entity which provides a common operating environment and human-machine interface for the operation and control of the orbiting Space Station systems and payloads by both the crew and the ground operators. This paper discusses the evolution and derivation of the requirements and issues which have had significant effect on the design of the Space Station DMS, describes the DMS components and services which support system and payload operations, and presents the current architectural view of the system as it exists in October 1986; one-and-a-half years into the Space Station Phase B Definition and Preliminary Design Study.

Mallary, William E.↗