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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.
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.
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.
Data processing and information transfer at National Space Science Data Center
The Design and Data Management System (DDMS) Project is a cooperative effort between Engineering and Information Systems whose overall objective is to move toward an integrated approach to collecting, managing, warehousing and accessing its engineering design data.
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.
Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.
Photographic data processing, management, and dissemination system to support Earth Resources Technology Satellite program - Vol. 3
As was revealed in a previous study, the Materials and Processes Laboratory's Productivity Enhancement Complex (PEC) has a number of automated production areas/cells that are not effectively integrated, limiting the ability of users to readily share data. The recent decision to utilize the PEC for the fabrication of flight hardware has focused new attention on the problem and brought to light the need for an integrated data management and scheduling system. This report addresses this need by developing preliminary designs specifications for a centralized manufacturing data management and scheduling system for managing flight hardware fabrication in the PEC. This prototype system will be developed under the auspices of the Integrated Engineering Environment (IEE) Oversight team and the IEE Committee. At their recommendation the system specifications were based on the fabrication requirements of the AXAF-S Optical Bench.
KSC is planning a Space Station Ground Data Management System (GDMS) for support of functional interface verification, integration and test of Space Station modules and elements. This computer system, planned for initial operational support in 1992, currently is entering a definition and prototyping stage. This paper provides an overview of the GDMS system concept. It synopsizes system functional capabilities, and discusses software and hardware architectural approaches currently under evaluation. It identifies programmatic constraints and their influence upon the concept, as well as specific technical issues planned for study or evaluation via prototyping.
The primary objective is to identify, develop, and demonstrate key data management technologies to support user access to Space Station data. To accomplish this objective, there are several technical challenges which must be addressed. The first is how to provide routine customer access to high volume, dynamic and distributed data bases. This access will encompass the functions of mission and payload planning and operations, data processing and analysis, and data archive and distribution. Secondly, there must be some analysis of architectures for handling high volume data streams like those expected from the Space Station. This analysis will examine the use of packetized versus non-packetized data formats, modular expansion capabilities, real-time versus non-real-time data processing, and the interfaces and architecture required to support telescience operations. The task will also determine benchmarks of performance capabilities for technology operations, such as varied data base structures, data access procedures, distributed data base design, and data base machines. Information is provided here in outline form.
Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
Design of central data system for advanced deep solar probe management
Advanced software techniques for space shuttle data management system
A summary of the findings of the data management group of the orbital operations study is presented. Element interfaces, alternate approaches, design concepts, operational procedures, functional requirements, design influences, and approach selection are described. The following interfacing activities are considered: (1) communications, (2) rendezvous, (3) stationkeeping, and (4) detached element operations.
The NOAA archives contain 150 terabytes of data in digital form, most of which are the high volume GOES satellite image data. There are 630 data bases containing 2,350 environmental variables. There are 375 million film records and 90 million paper records in addition to the digital data base. The current data accession rate is 10 percent per year and the number of users are increasing at a 10 percent annual rate. NOAA publishes 5,000 publications and distributes over one million copies to almost 41,000 paying customers. Each year, over six million records are key entered from manuscript documents and about 13,000 computer tapes and 40,000 satellite hardcopy images are entered into the archive. Early digital data were stored on punched cards and open reel computer tapes. In the late seventies, an advanced helical scan technology (AMPEX TBM) was implemented. Now, punched cards have disappeared, the TBM system was abandoned, most data stored on open reel tapes have been migrated to 3480 cartridges, many specialized data sets were distributed on CD ROM's, special archives are being copied to 12 inch optical WORM disks, 5 1/4 inch magneto-optical disks were employed for workstation applications, and 8 mm EXABYTE tapes are planned for major data collection programs. The rapid expansion of new data sets, some of which constitute large volumes of data, coupled with the need for vastly improved access mechanisms, portability, and improved longevity are factors which will influence NOAA's future systems approaches for data management.
The primary purpose of the report is to explore management approaches and technology developments for computation and data management systems designed to meet future needs in the space sciences.The report builds on work presented in previous reports on solar-terrestrial and planetary reports, broadening the outlook to all of the space sciences, and considering policy issues aspects related to coordiantion between data centers, missions, and ongoing research activities, because it is perceived that the rapid growth of data and the wide geographic distribution of relevant facilities will present especially troublesome problems for data archiving, distribution, and analysis.