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Data management applications

Kennedy Space Center's primary institutional computer is a 4 megabyte IBM 4341 with 3.175 billion characters of IBM 3350 disc storage. This system utilizes the Software AG product known as ADABAS with the on line user oriented features of NATURAL and COMPLETE as a Data Base Management System (DBMS). It is operational under the OS/VSI and is currently supporting batch/on line applications such as Personnel, Training, Physical Space Management, Procurement, Office Equipment Maintenance, and Equipment Visibility. A third and by far the largest DBMS application is known as the Shuttle Inventory Management System (SIMS) which is operational on a Honeywell 6660 (dedicated) computer system utilizing Honeywell Integrated Data Storage I (IDSI) as the DBMS. The SIMS application is designed to provide central supply system acquisition, inventory control, receipt, storage, and issue of spares, supplies, and materials.

Source record↗

Information management system: A summary discussion

An information management system is proposed for use in the space shuttle sortie, the modular space station, the tracking data relay satellite and associated ground support systems. Several different information management functions, including data acquisition, transfer, storage, processing, control and display are integrated in the system.

Sayers, R. S.↗

Virtual Reality Training: "Cybersickness" and Effects on Sensorimotor Functions

The overall goal of this study is to examine the extent to which exposure to virtual reality (VR) systems produces motion sickness and disrupts sensorimotor functions. Two of the major problems in using VRs are: 1) potential "cybersickness", a form of motion sickness, and 2) maladaptive sensorimotor coordination following virtual environment (VE) training. It is likely that users will eventually adapt to any unpleasant perceptual experiences in a virtual environment. However the most critical problem for training applications is that sensorimotor coordination strategies learned in the VE may not be similar to the responses required in the real environment. This study will evaluate and compare responses to the two types of VR delivery systems (head-mounted display [HMD] and a dome-projection system [DOME]), two exposure duration periods (30 minutes or 60 minutes), and repeated exposures (3 sessions). Specific responses that we will examine include cybersickness severity and symptom patterns, and several sensorimotor functions (eye-hea.d and eye-head-hand coordination, and postural equilibrium). To date, all hardware and software acquisition, development, integration and testing has been completed. A database has been developed and tested for the input, management and storage of all questionnaire data. All data analysis scripts have been developed and tested. Data was collected from 20 subjects in a pilot study that was conducted to determine the amount of training necessary to achieve a stable performance level. Seven subjects are currently enrolled in the study designed to examine the effects of exposure to VE systems on postural control. Data has been collected from two subjects, and it is expected that the results from ten subjects will be presented.

Harm, Deborah L.↗

NCDC mass storage systems and technologies

National Climatic Data Center (NCDC) data management issues are discussed, such as nature of holdings, history of the site, popularity of data sets, media/technology used for storage, volume distributed per month, mode of distribution, most frequently encountered problems, type of media requested/used, and evolution of media. Current holdings at NCDC are 107.8 terabytes of digital data and about 0.3 terabytes of manuscript data. The nexrad radar system is expected to generate approximately 88 terabytes per year by 1996.

Davis, Dick↗

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.

Johnston, William E.↗

Preservation Environments

The long-term preservation of digital entities requires mechanisms to manage the authenticity of massive data collections that are written to archival storage systems. Preservation environments impose authenticity constraints and manage the evolution of the storage system technology by building infrastructure independent solutions. This seeming paradox, the need for large archives, while avoiding dependence upon vendor specific solutions, is resolved through use of data grid technology. Data grids provide the storage repository abstractions that make it possible to migrate collections between vendor specific products, while ensuring the authenticity of the archived data. Data grids provide the software infrastructure that interfaces vendor-specific storage archives to preservation environments.

Moore, Reagan W.↗

FJET Database Project: Extract, Transform, and Load

The Data Mining & Knowledge Management team at Kennedy Space Center is providing data management services to the Frangible Joint Empirical Test (FJET) project at Langley Research Center (LARC). FJET is a project under the NASA Engineering and Safety Center (NESC). The purpose of FJET is to conduct an assessment of mild detonating fuse (MDF) frangible joints (FJs) for human spacecraft separation tasks in support of the NASA Commercial Crew Program. The Data Mining & Knowledge Management team has been tasked with creating and managing a database for the efficient storage and retrieval of FJET test data. This paper details the Extract, Transform, and Load (ETL) process as it is related to gathering FJET test data into a Microsoft SQL relational database, and making that data available to the data users. Lessons learned, procedures implemented, and programming code samples are discussed to help detail the learning experienced as the Data Mining & Knowledge Management team adapted to changing requirements and new technology while maintaining flexibility of design in various aspects of the data management project.

excel vba↗

Optimizing tertiary storage organization and access for spatio-temporal datasets

We address in this paper data management techniques for efficiently retrieving requested subsets of large datasets stored on mass storage devices. This problem represents a major bottleneck that can negate the benefits of fast networks, because the time to access a subset from a large dataset stored on a mass storage system is much greater that the time to transmit that subset over a network. This paper focuses on very large spatial and temporal datasets generated by simulation programs in the area of climate modeling, but the techniques developed can be applied to other applications that deal with large multidimensional datasets. The main requirement we have addressed is the efficient access of subsets of information contained within much larger datasets, for the purpose of analysis and interactive visualization. We have developed data partitioning techniques that partition datasets into 'clusters' based on analysis of data access patterns and storage device characteristics. The goal is to minimize the number of clusters read from mass storage systems when subsets are requested. We emphasize in this paper proposed enhancements to current storage server protocols to permit control over physical placement of data on storage devices. We also discuss in some detail the aspects of the interface between the application programs and the mass storage system, as well as a workbench to help scientists to design the best reorganization of a dataset for anticipated access patterns.

Chen, Ling Tony↗

Simulating Secure Data Exchange and Storage for Urban Air Mobility Environments

Urban Air Mobility (UAM) defines an environment for managing operations of vertical takeoff and landing (VTOL) and short takeoff and landing (STOL) vehicles in an urban environment. Within a UAM environment, UAM operators manage fleets of vehicles, relying on Providers of Services for UAM (PSUs) for managing flights in a region of airspace. Flight plan deconfliction is primarily performed by the Discovery and Synchronization Service (DSS), and the Federal Aviation Administration (FAA) maintains control over the UAM space via the FAA-Industry Exchange Protocol (FIDXP). UAM is a federated environment with many different entities owning and operating vehicles, PSUs, and other services. These entities often need to interoperate or access data generated by other organizations. This paper demonstrates the feasibility of using blockchain to facilitate a secure data exchange and storage for this flight information in a UAM environment. In particular, this paper is focused on flight plans and telemetry data. A blockchain network was developed with a set of smart contracts for managing relevant flight data. Hyperledger Fabric was chosen as it is performent, scalable, and allows organizations to reuse existing public key infrastructure (PKI) for identity management. A set of simulated UAM services were also developed. These services propose flight plans and negotiate with other UAM services for airspace access. All interactions between UAM services, as well as vehicle telemetry data, is recorded onto the blockchain. Vehicle telemetry data is generated by a vehicle flight simulation service. This paper successfully demonstrates the feasibility of using blockchain as a secure data exchange and storage mechanism in a UAM environment.

UAM↗

The Kepler End-to-End Data Pipeline: From Photons to Far Away Worlds

The Kepler mission is described in overview and the Kepler technique for discovering exoplanets is discussed. The design and implementation of the Kepler spacecraft, tracing the data path from photons entering the telescope aperture through raw observation data transmitted to the ground operations team is described. The technical challenges of operating a large aperture photometer with an unprecedented 95 million pixel detector are addressed as well as the onboard technique for processing and reducing the large volume of data produced by the Kepler photometer. The technique and challenge of day-to-day mission operations that result in a very high percentage of time on target is discussed. This includes the day to day process for monitoring and managing the health of the spacecraft, the annual process for maintaining sun on the solar arrays while still keeping the telescope pointed at the fixed science target, the process for safely but rapidly returning to science operations after a spacecraft initiated safing event and the long term anomaly resolution process.The ground data processing pipeline, from the point that science data is received on the ground to the presentation of preliminary planetary candidates and supporting data to the science team for further evaluation is discussed. Ground management, control, exchange and storage of Kepler's large and growing data set is discussed as well as the process and techniques for removing noise sources and applying calibrations to intermediate data products.

data archiving↗

Spaceborne optical disk controller development

The current status and potential applications of an optical-disk buffer (ODB) memory system being developed by an interagency consortium including NASA and the USAF are reviewed. The design goals for the ODB include usable capacity 1 Tb, maximum data rate 1.6 Gb/s, read error rate less than 10 to the -12th, time to initial access less than 100 ms, and unlimited read/write cycles. Present efforts focus on a brassboard ODB which employs 12 14-inch magnetooptic disks and 24 nine-diode read/write heads. A typical space application of an optical disk mass memory system (ODMMS) is discussed: as communications buffer, temporary storage, and/or multiuser I/O buffer for data management on the Space Station Earth Observing System. Environmental, operational, system-architecture, and functional-separation factors; critical design issues; and standardization questions for spaceborne ODMMSs are examined in detail.

Shull, Thomas A.↗

Network accessible multi-terabyte archive

The viewgraphs of a discussion on Network Accessible Multi-terabyte Archive presented at the National Space Science Data Center (NSSDC) Mass Storage Workshop is included. Topics covered in the presentation include the rotary storage system (RSS) including RSS data access, data management, administration, and hardware and software architecture.

Rybczynski, Fred↗

A Spacebased Ocean Surface Exchange Data Analysis System

Emerging technologies have provided unprecedented opportunities to transform information into knowledge and disseminate them in a much faster, cheaper, and userfriendly mode. We have set up a system to produce and disseminate high level (gridded) ocean surface wind data from the NASA Scatterometer and European Remote Sensing missions. The data system is being expanded to produce real-time gridded ocean surface winds from an improved sensor SeaWinds on the Quikscat Mission. The wind field will be combined with hydrologic parameters from the Tropical Rain Measuring Mission to monitor evolving weather systems and natural hazard in real time. It will form the basis for spacebased Ocean Surface Exchange Data Analysis System (SOSEDAS) which will include the production of ocean surface momentum, heat, and water fluxes needed for interdisciplinary studies of ocean-atmosphere interaction. Various commercial or non-commercial software tools have been compared and selected in terms of their ability in database management, remote data accessing, graphical interface, data quality, storage needs and transfer speed, etc. Issues regarding system security and user authentication, distributed data archiving and accessing, strategy to compress large-volume geophysical and satellite data/image. and increasing transferring speed are being addressed. A simple and easy way to access information and derive knowledge from spacebased data of multiple missions is being provided. The evolving 'knowledge system' will provide relevant infrastructure to address Earth System Science, make inroads in educating an informed populace, and illuminate decision and policy making.

Tang, Wenqing↗

Collectives for Multiple Resource Job Scheduling Across Heterogeneous Servers

Efficient management of large-scale, distributed data storage and processing systems is a major challenge for many computational applications. Many of these systems are characterized by multi-resource tasks processed across a heterogeneous network. Conventional approaches, such as load balancing, work well for centralized, single resource problems, but breakdown in the more general case. In addition, most approaches are often based on heuristics which do not directly attempt to optimize the world utility. In this paper, we propose an agent based control system using the theory of collectives. We configure the servers of our network with agents who make local job scheduling decisions. These decisions are based on local goals which are constructed to be aligned with the objective of optimizing the overall efficiency of the system. We demonstrate that multi-agent systems in which all the agents attempt to optimize the same global utility function (team game) only marginally outperform conventional load balancing. On the other hand, agents configured using collectives outperform both team games and load balancing (by up to four times for the latter), despite their distributed nature and their limited access to information.

Tumer, K.↗

Optical mass memory system (AMM-13). AMM-13 system segment specification

The performance, design, development, and test requirements for an optical mass data storage and retrieval system prototype (AMM-13) are established. This system interfaces to other system segments of the NASA End-to-End Data System via the Data Base Management System segment and is designed to have a storage capacity of 10 to the 13th power bits (10 to the 12th power bits on line). The major functions of the system include control, input and output, recording of ingested data, fiche processing/replication and storage and retrieval.

Bailey, G. A.↗

GSE, data management system programmers/User' manual

The GSE data management system is a computerized program which provides for a central storage source for key data associated with the mechanical ground support equipment (MGSE). Eight major sort modes can be requested by the user. Attributes that are printed automatically with each sort include the GSE end item number, description, class code, functional code, fluid media, use location, design responsibility, weight, cost, quantity, dimensions, and applicable documents. Multiple subsorts are available for the class code, functional code, fluid media, use location, design responsibility, and applicable document categories. These sorts and how to use them are described. The program and GSE data bank may be easily updated and expanded.

Schlagheck, R. A.↗

Mass storage at NSA

The need to manage large amounts of data on robotically controlled devices has been critical to the mission of this Agency for many years. In many respects this Agency has helped pioneer, with their industry counterparts, the development of a number of products long before these systems became commercially available. Numerous attempts have been made to field both robotically controlled tape and optical disk technology and systems to satisfy our tertiary storage needs. Custom developed products were architected, designed, and developed without vendor partners over the past two decades to field workable systems to handle our ever increasing storage requirements. Many of the attendees of this symposium are familiar with some of the older products, such as: the Braegen Automated Tape Libraries (ATL's), the IBM 3850, the Ampex TeraStore, just to name a few. In addition, we embarked on an in-house development of a shared disk input/output support processor to manage our every increasing tape storage needs. For all intents and purposes, this system was a file server by current definitions which used CDC Cyber computers as the control processors. It served us well and was just recently removed from production usage.

Shields, Michael F.↗

Petabyte Class Storage at Jefferson Lab (CEBAF)

By 1997, the Thomas Jefferson National Accelerator Facility will collect over one Terabyte of raw information per day of Accelerator operation from three concurrently operating Experimental Halls. When post-processing is included, roughly 250 TB of raw and formatted experimental data will be generated each year. By the year 2000, a total of one Petabyte will be stored on-line. Critical to the experimental program at Jefferson Lab (JLab) is the networking and computational capability to collect, store, retrieve, and reconstruct data on this scale. The design criteria include support of a raw data stream of 10-12 MB/second from Experimental Hall B, which will operate the CEBAF (Continuous Electron Beam Accelerator Facility) Large Acceptance Spectrometer (CLAS). Keeping up with this data stream implies design strategies that provide storage guarantees during accelerator operation, minimize the number of times data is buffered allow seamless access to specific data sets for the researcher, synchronize data retrievals with the scheduling of postprocessing calculations on the data reconstruction CPU farms, as well as support the site capability to perform data reconstruction and reduction at the same overall rate at which new data is being collected. The current implementation employs state-of-the-art StorageTek Redwood tape drives and robotics library integrated with the Open Storage Manager (OSM) Hierarchical Storage Management software (Computer Associates, International), the use of Fibre Channel RAID disks dual-ported between Sun Microsystems SMP servers, and a network-based interface to a 10,000 SPECint92 data processing CPU farm. Issues of efficiency, scalability, and manageability will become critical to meet the year 2000 requirements for a Petabyte of near-line storage interfaced to over 30,000 SPECint92 of data processing power.

Chambers, Rita↗