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Configuration and Data Management Process and the System Safety Professional

This article presents a discussion of the configuration management (CM) and the Data Management (DM) functions and provides a perspective of the importance of configuration and data management processes to the success of system safety activities. The article addresses the basic requirements of configuration and data management generally based on NASA configuration and data management policies and practices, although the concepts are likely to represent processes of any public or private organization's well-designed configuration and data management program.

Shivers, Charles Herbert

Space Station Freedom (SSF) Data Management System (DMS) performance model data base

The purpose of this document was originally to be a working document summarizing Space Station Freedom (SSF) Data Management System (DMS) hardware and software design, configuration, performance and estimated loading data from a myriad of source documents such that the parameters provided could be used to build a dynamic performance model of the DMS. The document is published at this time as a close-out of the DMS performance modeling effort resulting from the Clinton Administration mandated Space Station Redesign. The DMS as documented in this report is no longer a part of the redesigned Space Station. The performance modeling effort was a joint undertaking between the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Flight Data Systems Division (FDSD) and the NASA Ames Research Center (ARC) Spacecraft Data Systems Research Branch. The scope of this document is limited to the DMS core network through the Man Tended Configuration (MTC) as it existed prior to the 1993 Clinton Administration mandated Space Station Redesign. Data is provided for the Standard Data Processors (SDP's), Multiplexer/Demultiplexers (MDM's) and Mass Storage Units (MSU's). Planned future releases would have added the additional hardware and software descriptions needed to describe the complete DMS. Performance and loading data through the Permanent Manned Configuration (PMC) was to have been included as it became available. No future releases of this document are presently planned pending completion of the present Space Station Redesign activities and task reassessment.

Stovall, John R.

The 1970/71 spectral data management programs

The data management programs used by the Stanford Remote Sensing Laboratory to access, modify, and reduce the data obtained from both the NASA IR airborne spectrometer, and Stanford's SG-4 field spectrometer are reported. Many details covered in previous reports are not repeated. References are provided. These programs are written in FORTRAN 4 and S/360 Assembler Language, and are currently running on a S/360 model 67 (operating under OS/MFT) at the Stanford Computation Center Campus Facility.

Marshall, A. A.

A guide to onboard checkout. Volume 5: Data management

The baseline data management subsystem for a space station is discussed. The subsystem consists of equipment necessary to transfer, store, and process data to and from users and subsystems. It acquires and conditions a wide variety of input data from experiments, vehicle subsystems sensors, uplinked ground communications, and astronaut-activated controls. Computer techniques for failure analysis, reliability, and maintenance checkout onboard the space station are considered.

Source record

Langley applications experiments data management system study

A data management system study is presented that defines, in functional terms, the most cost effective ground data management system to support Advanced Technology Laboratory (ATL) flights of the space shuttle. Results from each subtask performed and the recommended system configuration for reformatting the experiment instrumentation tapes to computer compatible tape are examined. Included are cost factors for development of a mini control center for real-time support of the ATL flights.

Lanham, C. C., Jr.

Redundant data management system

Redundant data management system solves problem of operating redundant equipment in real time environment where failures are detected, isolated, and switched in simple manner. System consists of quadruply-redundant computer, input/output control units, and data buses. System inherently contains failure detection, isolation, and switching function.

Hall, J. R.

Modeling of the Space Station Freedom data management system

The data management system (DMS) is the information and communications system onboard Space Station Freedom. Extensive modeling of the DMS is being conducted throughout NASA to aid in the design and development of this vital system. Activities to model the DMS network infrastructure are described, focusing on modeling of the fiber distributed data interface (FDDI) token-ring protocol and experimental testbedding of networking aspects of the DMS.

Johnson, Marjory J.

Modeling of the Space Station Freedom data management system

The Data Management System (DMS) is the information and communications system onboard Space Station Freedom (SSF). Extensive modeling of the DMS is being conducted throughout NASA to aid in the design and development of this vital system. Activities discussed at NASA Ames Research Center to model the DMS network infrastructure are discussed with focus on the modeling of the Fiber Distributed Data Interface (FDDI) token-ring protocol and experimental testbedding of networking aspects of the DMS.

Johnson, Marjory J.

Data management support for selected climate data sets using the climate data access system

The functional capabilities of the Goddard Space Flight Center (GSFC) Climate Data Access System (CDAS), an interactive data storage and retrieval system, and the archival data sets which this system manages are discussed. The CDAS manages several climate-related data sets, such as the First Global Atmospheric Research Program (GARP) Global Experiment (FGGE) Level 2-b and Level 3-a data tapes. CDAS data management support consists of three basic functions: (1) an inventory capability which allows users to search or update a disk-resident inventory describing the contents of each tape in a data set, (2) a capability to depict graphically the spatial coverage of a tape in a data set, and (3) a data set selection capability which allows users to extract portions of a data set using criteria such as time, location, and data source/parameter and output the data to tape, user terminal, or system printer. This report includes figures that illustrate menu displays and output listings for each CDAS function.

Reph, M. G.

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management

Centralized Data Management Platform

The technology is an adaptive data management and integration platform designed for disparate data sources. It is built to support multitenancy, manage data governance, handle heterogeneous data formats and advance data democratization using a suite of connected, independent microservices. Each service can be used within an integrated environment, or as a standalone product, with a dedicated set of functionalities, such as metadata management, data versioning, access control, data tagging, link management, and analytics, among others.

Technology Transfer

Analysis of the Apollo spacecraft operational data management system. Executive summary

A study was made of Apollo, Skylab, and several other data management systems to determine those techniques which could be applied to the management of operational data for future manned spacecraft programs. The results of the study are presented and include: (1) an analysis of present data management systems, (2) a list of requirements for future operational data management systems, (3) an evaluation of automated data management techniques, and (4) a plan for data management applicable to future space programs.

Source record

Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management

Data management, archiving, visualization and analysis of space physics data

A series of programs for the visualization and analysis of space physics data has been developed at UCLA. In the course of those developments, a number of lessons have been learned regarding data management and data archiving, as well as data analysis. The issues now facing those wishing to develop such software, as well as the lessons learned, are reviewed. Modern media have eased many of the earlier problems of the physical volume required to store data, the speed of access, and the permanence of the records. However, the ultimate longevity of these media is still a question of debate. Finally, while software development has become easier, cost is still a limiting factor in developing visualization and analysis software.

Russell, C. T.