A Semantic Representation of Product Quality and Evidence for Satellite Data

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Presented at the AGU Fall Meeting 2011

Abstract:

There is growing interest within the broad research community to leverage satellite data for cross-disciplinary analysis and to make use of the data in ways unanticipated by the data provider. Poorly documented or publicized product quality information is a significant barrier to the successful or confident integration of satellite data for many users. Researchers seek clearly and consistently characterized product quality to facilitate assessment of product fitness-for-use. We argue that data product discovery mechanisms should be augmented with facilities to present product quality information; targeted to provide a condensed and clear view of product quality and to support comparison with quality of other like products. We propose a method of provisioning product quality into aspects (e.g. completeness, consistency, accuracy, bias) and displaying computed and inferred facts as evidence to help characterize one or more aspects of the product quality. We describe the product quality ontology developed to facilitate this characterization of product quality. Finally, we illustrate the utility of this approach by showing how we have applied it to presenting product quality for the NASA MODIS Aerosol data product within a prototype implementation of the NASA Giovanni Data Access and Analysis Tool.

History

DateCreated ByLink
December 4, 2011
23:25:57
Stephan ZednikDownload
December 2, 2011
11:29:06
Stephan ZednikDownload

Related Projects:

MDSA LogoMulti-Sensor Data Synergy Advisor (MDSA)
Principal Investigator: Peter Fox
Description: Augment Giovanni, the Goddard online tool for data access, visualization and analysis, with semantic web technologies and ontologies to support data inter-comparisons from different sensors or models. Data provenance (i.e. the essential data parameter details, quality and production caveats) will be added to enable researchers to make valid data comparisons and draw quantitative conclusions on specific analysis (e.g. ocean fertilization due to acid rain). In the resulting Giovanni framework, the dataset variable characteristics and related quality can be encoded so that inter-comparison rules can be derived.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

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Concepts: eScience
Semantic eScience
Lead Professor: Peter Fox
Description:
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience