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Smarter Earth Science Data SystemThe explosive growth in Earth observational data in the recent decade demands a better method of interoperability across heterogeneous systems. The Earth science data system community has mastered the art in storing large volume of observational data, but it is still unclear how this traditional method scale over time as we are entering the age of Big Data. Indexed search solutions such as Apache Solr (Smiley and Pugh, 2011) provides fast, scalable search via keyword or phases without any reasoning or inference. The modern search solutions such as Googles Knowledge Graph (Singhal, 2012) and Microsoft Bing, all utilize semantic reasoning to improve its accuracy in searches. The Earth science user community is demanding for an intelligent solution to help them finding the right data for their researches. The Ontological System for Context Artifacts and Resources (OSCAR) (Huang et al., 2012), was created in response to the DARPA Adaptive Vehicle Make (AVM) programs need for an intelligent context models management system to empower its terrain simulation subsystem. The core component of OSCAR is the Environmental Context Ontology (ECO) is built using the Semantic Web for Earth and Environmental Terminology (SWEET) (Raskin and Pan, 2005). This paper presents the current data archival methodology within a NASA Earth science data centers and discuss using semantic web to improve the way we capture and serve data to our users.
Document ID
20150007130
Document Type
Conference Paper
External Source(s)
Authors
Huang, Thomas (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 1, 2015
Publication Date
September 19, 2013
Subject Category
Documentation and Information Science
Computer Programming and Software
Geosciences (General)
Meeting Information
INSTICC International Conference on Knowledge Engineering and Ontology Development (KEOD 2013)(Vilamoura, Algarve)
Distribution Limits
Public
Copyright
Other
Keywords
data center
reasoning
semantic web