Engineering topics

McConaughy, Gail

Publications and source records attributed to McConaughy, Gail.

5 records

NASA NTRS2005

A Testbed Demonstration of an Intelligent Archive in a Knowledge Building System

The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.

Ramapriyan, Hampapuram↗

NASA NTRS2004

Intelligent Archives in the Context of Knowledge Building Systems: Concepts for the Future

The technical objectives are: 1. Formulate concepts and architectures that support data archiving for NASA science research and applications in the 10 to 20 year time frame. 2. Focus on architectural strategies that will support intelligent processes and functions. 3. Identify and characterize science and applications scenarios that drive intelligent archive requirements. 4. Assess technologies and research that will need the development of an intelligent archive. 5. Identify and characterize potential research projects that will be needed to develop and create an intelligent archive.

Ramapriyan, H. K.↗

NASA NTRS2003

Intelligent Systems Technologies to Assist in Utilization of Earth Observation Data

With the launch of several Earth observing satellites over the last decade, we are now in a data rich environment. From NASA's Earth Observing System (EOS) satellites alone, we are accumulating more than 3 TB per day of raw data and derived geophysical parameters. The data products are being distributed to a large user community comprising scientific researchers, educators and operational government agencies. Notable progress has been made in the last decade in facilitating access to data. However, to realize the full potential of the growing archives of valuable scientific data, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications. Sponsored by NASA s Intelligent Systems Project within the Computing, Information and Communication Technology (CICT) Program, a conceptual architecture study has been conducted to examine ideas to improve data utilization through the addition of intelligence into the archives in the context of an overall knowledge building system. Potential Intelligent Archive concepts include: 1) Mining archived data holdings using Intelligent Data Understanding algorithms to improve metadata to facilitate data access and usability; 2) Building intelligence about transformations on data, information, knowledge, and accompanying services involved in a scientific enterprise; 3) Recognizing the value of results, indexing and formatting them for easy access, and delivering them to concerned individuals; 4) Interacting as a cooperative node in a web of distributed systems to perform knowledge building (i.e., the transformations from data to information to knowledge) instead of just data pipelining; and 5) Being aware of other nodes in the knowledge building system, participating in open systems interfaces and protocols for virtualization, and collaborative interoperability. This paper presents some of these concepts and identifies issues to be addressed by research in future intelligent systems technology.

Ramapriyan, Hampapuram K.↗

NASA NTRS2002

Conceptual Study of Intelligent Data Archives of the Future

A conceptual architecture study is under way to address the problem of getting the most scientific value from the large volumes of Earth and space science data that NASA expects to accumulate in the future. This involves efficient storage and access, but goes beyond that to facilitate intelligent data understanding and utilization through modeling realistic virtual entities with predictive capabilities. The objective of the study is to formulate ideas and concepts and to provide recommendations that lead to prototyping and implementation in the period from 2010 to 2020. The approach consists of the definition of future scenarios and needs for data usage in applications (in consultation with scientific and applications users), projection of advances in technologies, and an abstraction of an intelligent archive architecture. Strategic evolution is considered in various areas such as storage, data, information and knowledge management, data ingest and mining, user interfaces, and advances in intelligent data understanding algorithms.

Ramapriyan, H. K.↗

NASA NTRS2000

The Earth Science Vision

NASA's Earth Science Enterprise's long range vision is to enable the development of a national proactive environmental predictive capability through targeted scientific research and technological innovation. Proactive environmental prediction means the prediction of environmental events and their secondary consequences. These consequences range from disasters and disease outbreak to improved food production and reduced transportation, energy and insurance costs. The economic advantage of this predictive capability will greatly outweigh the cost of development. Developing this predictive capability requires a greatly improved understanding of the earth system and the interaction of the various components of that system. It also requires a change in our approach to gathering data about the earth and a change in our current methodology in processing that data including its delivery to the customers. And, most importantly, it requires a renewed partnership between NASA and its sister agencies. We identify six application themes that summarize the potential of proactive environmental prediction. We also identify four technology themes that articulate our approach to implementing proactive environmental prediction.

Schoeberl, Mark↗