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Overview

Multi-Sensor Data Synergy Advisor is a NASA ESTO sponsored project

Motivation

The NRC Decadal Survey proposed GEOstationary Coastal and Air Pollution Events (GEO-CAPE) mission is designed to make multiple, simultaneous observations of atmospheric and oceanic properties several times a day from geosynchronous orbit. The Multi-Sensor Data Synergy Advisor (MSDSA) is a framework, including software, ontologies, rulesets and related tools, which aims to advise and help NASA NRC Decadal Survey missions data users in a number of operations involving multiple datasets, such as data merging, cross-calibration, validation, cross-comparison and fusion. MDSA allows users to combine and compare data from multiple sensors, such that scientifically and statistically valid conclusions can be drawn.

 

This project builds an ontological framework whereby the characteristics of dataset variables and their related qualities are encoded so that intercomparison rules can be derived. Semantic Web technologies and ontologies are used to capture essential parameter details, quality and caveats. A rich interlingua (Proof Markup Language) and tools from the Inference Web project are then used to capture inter-relations of the provenance. Only the valid comparisons or the caveats regarding speculative comparisons are presented, enabling users to evaluate potential inter-comparisons as valid, speculative or invalid, including an explanation of the result.

 

MSDSA is being implemented using the GES DISC Giovanni online data access and visualization tool as a testbed. However, the ontologies and rulesets are by nature reusable and interoperable with other applications that are semantically enabled, thus paving the way for eventual incorporation of MSDSA in larger “systems of systems” such as GEOSS.

 

Objectives

  • 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

Approach

• Capture scientist knowledge (rulesets) of the science & data quality characteristics
• Encode this knowledge so a computer can retrieve it
• Present only the safe comparisons, or the caveats for speculative comparisons
• Provide user-tunable quality screening
• Generate the Giovanni workflow and record the associated provenance

Project members

Principal Investigator:

Gregory Leptoukh, GSFC

Co-Investigators:

Peter Fox, RPI
Christopher Lynnes, GSFC
Ana Prados, UMBC/GSFC
Suhung Shen, GMU/GSFC

Active members:

Hualan Rui, Adnet/GSFC
Jianfu Pan, Adnet/GSFC
Patrick West, RPI
Stephan Zedink, RPI

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Last updated: Sep 09, 2009 05:10 PM ET
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