Peter B. Tran

NETMARK: A schema-less extension for relational databases for managing semi-structured data dynamically

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semi-structured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML.

Articulation management for intelligent integration of information

When combining data from distinct sources, there is a need to share meta-data and other knowledge about various source domains. Due to semantic inconsistencies and heterogeneity of representations, problems arise in combining multiple domains when the domains are merged. The knowledge that is irrelevant to the task of interoperation will be included, making the result unnecessarily complex. This heterogeneity problem can be eliminated by mediating the conflicts and managing the intersections of the domains. For interoperation and intelligent access to heterogeneous information, the focus is on the intersection of the knowledge. An algebra over domain is proposed which uses articulation rules to support disciplined manipulation of domain knowledge resources. The objective of a domain algebra is to provide the capability for interrogating many domain knowledge resources. The algebra formally supports the tasks of selecting, combining, extending, specializing, and modifying components from a diverse set of domains. The paper presents a domain algebra and demonstrates the use of articulation rules to link declarative interfaces for Internet and enterprise applications. In particular, it discusses the articulation implementation as part of a production system capable of operating over the domain described by the interface description language (IDL) of objects registered in multiple CORBA servers.

Effective Data Representation and Compression in Ground Data Systems

Storing vast amounts of multidimensional telemetry data presents a challenge. Telemetry data being relayed from sensors to the ground station comes in the form of text, images, audio, and various other formats. Compressing this data would optimize bandwidth usage during transmission and reduce storage resources needed at the ground level. However, the multitude of heterogeneous data types present in telemetry data and the need for data precision makes compression quite difficult. The application of a single compression technique for all data types usually yields ineffective results. We will present a telemetry data compression algorithm that utilizes Discrete Fourier Transforms (DFTs) along with different compression algorithms for different data types, including Lempel-Ziv-Welch (LZW) and Flate (which combines LZW with adaptive Huffman coding) for textual and numerical data and JPEG coding for images. Although these algorithms do not yield the greatest compression ratios, the Portable Document Format (PDF) standard supports decoding of all of them, which allows us to write our encoded data streams directly to a PDF file. This approach alleviates the need for traditional database storage systems. It also standardizes and simplifies the data retrieval, decoding, and viewing process. This work results in packets-oriented telemetry data encapsulated with multiple compression stream algorithms, which can be decoded, rendered and viewed by any standard PDF viewer. This paper presents the aforementioned algorithms and its development status as applicable proof-of-concept prototypes.

Secure Large-Scale Airport Simulations Using Distributed Computational Resources

To fully conduct research that will support the far-term concepts, technologies and methods required to improve the safety of Air Transportation a simulation environment of the requisite degree of fidelity must first be in place. The Virtual National Airspace Simulation (VNAS) will provide the underlying infrastructure necessary for such a simulation system. Aerospacespecific knowledge management services such as intelligent data-integration middleware will support the management of information associated with this complex and critically important operational environment. This simulation environment, in conjunction with a distributed network of super-computers, and high-speed network connections to aircraft, and to Federal Aviation Administration (FAA), airline and other data-sources will provide the capability to continuously monitor and measure operational performance against expected performance. The VNAS will also provide the tools to use this performance baseline to obtain a perspective of what is happening today and of the potential impact of proposed changes before they are introduced into the system.

Airport Remote Tower Sensor Systems

Networks of video cameras, meteorological sensors, and ancillary electronic equipment are under development in collaboration among NASA Ames Research Center, the Federal Aviation Administration (FAA), and the National Oceanic Atmospheric Administration (NOAA). These networks are to be established at and near airports to provide real-time information on local weather conditions that affect aircraft approaches and landings. The prototype network is an airport-approach-zone camera system (AAZCS), which has been deployed at San Francisco International Airport (SFO) and San Carlos Airport (SQL). The AAZCS includes remotely controlled color video cameras located on top of SFO and SQL air-traffic control towers. The cameras are controlled by the NOAA Center Weather Service Unit located at the Oakland Air Route Traffic Control Center and are accessible via a secure Web site. The AAZCS cameras can be zoomed and can be panned and tilted to cover a field of view 220 wide. The NOAA observer can see the sky condition as it is changing, thereby making possible a real-time evaluation of the conditions along the approach zones of SFO and SQL. The next-generation network, denoted a remote tower sensor system (RTSS), will soon be deployed at the Half Moon Bay Airport and a version of it will eventually be deployed at Los Angeles International Airport. In addition to remote control of video cameras via secure Web links, the RTSS offers realtime weather observations, remote sensing, portability, and a capability for deployment at remote and uninhabited sites. The RTSS can be used at airports that lack control towers, as well as at major airport hubs, to provide synthetic augmentation of vision for both local and remote operations under what would otherwise be conditions of low or even zero visibility.

User-Driven Collaboration for NASA Mission Control

NASA Ames, in conjunction with the Johnson Space Center (JSC), is building a platform to enable mission control operations software to be assembled from flexible collections of components and services. MCT is designed to support rapid creation, composition, visualization, and certification of user objects, while using social media techniques to help communicate, categorize, and analyze the massive amount of data and complex processes required for space flight operations. Since its initial deployment, MCT has helped spur collaboration within and across NASA centers, allowing different teams to work together on the shared goal of safe space flight. Flight controllers at JSC are assembling, tagging, searching, and visualizing telemetry. Software developers are extending the platform by integrating new data sources and analysis capabilities while reusing high-level functionality, such as scalable real-time plotting. Finally, the process of adopting MCT across NASA centers has opened communication channels through shared software development, feature design, and work practices.

NETMARK: Adding Hierarchical Object to Relational Databases with “Schema-less” Extensions

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semi-structured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML.

Effective document-oriented telemetry data compression

Storing vast amounts of multidimensional telemetry data presents a challenge. Telemetry data being relayed from sensors to the ground station comes in the form of text, images, audio, and various other formats. Compressing this data would optimize bandwidth usage during transmission and reduce storage resources needed at the ground level. The application of a single compression technique for all data types usually yields ineffective results. We will present a telemetry data compression algorithm that utilizes Discrete Fourier Transforms (DFTs) along with different compression algorithms for different data types, including Lempel-Ziv-Welch (LZW) and Flate for textual and numerical data and JPEG coding for images. Although these algorithms do not yield the greatest compression ratios, the Portable Document Format (PDF) standard supports decoding of all of them, which allows us to write our encoded data streams directly to a PDF file. This approach alleviates the need for traditional database storage systems. It also standardizes and simplifies the data retrieval, decoding, and viewing process. This work results in packets-oriented telemetry data encapsulated with multiple compression stream algorithms, which can be decoded, rendered and viewed by any standard PDF viewer. This paper presents the aforementioned algorithms and its development status as applicable proof-of-concept prototypes.

Sensing Super-position: Human Sensing Beyond the Visual Spectrum

The coming decade of fast, cheap and miniaturized electronics and sensory devices opens new pathways for the development of sophisticated equipment to overcome limitations of the human senses. This paper addresses the technical feasibility of augmenting human vision through sensing super-position by mixing natural human sensing. The current implementation of the device translates visual and other passive or active sensory instruments into sounds, which become relevant when the visual resolution is insufficient for very difficult and particular sensing tasks. A successful sensing super-position meets many human and pilot vehicle system requirements. The system can be further developed into cheap, portable, and low power taking into account the limited capabilities of the human user as well as the typical characteristics of his dynamic environment. The system operates in real time, giving the desired information for the particular augmented sensing tasks. The sensing super-position device increases the image resolution perception and is obtained via an auditory representation as well as the visual representation. Auditory mapping is performed to distribute an image in time. The three-dimensional spatial brightness and multi-spectral maps of a sensed image are processed using real-time image processing techniques (e.g. histogram normalization) and transformed into a two-dimensional map of an audio signal as a function of frequency and time. This paper details the approach of developing sensing super-position systems as a way to augment the human vision system by exploiting the capabilities of the human hearing system as an additional neural input. The human hearing system is capable of learning to process and interpret extremely complicated and rapidly changing auditory patterns. The known capabilities of the human hearing system to learn and understand complicated auditory patterns provided the basic motivation for developing an image-to-sound mapping system. The human brain is superior to most existing computer systems in rapidly extracting relevant information from blurred, noisy, and redundant images. From a theoretical viewpoint, this means that the available bandwidth is not exploited in an optimal way. While image-processing techniques can manipulate, condense and focus the information (e.g., Fourier transforms), keeping the mapping as direct and simple as possible might also reduce the risk of accidentally filtering out important clues. After all, especially a perfect non-redundant sound representation is prone to loss of relevant information in the non-perfect human hearing system. Also, a complicated non-redundant image-to-sound mapping may well be far more difficult to learn and comprehend than a straightforward mapping, while the mapping system would increase in complexity and cost. This work demonstrates some basic information processing for optimal information capture for head-mounted systems