Using Semantic Web Technologies to Streamline the Implementation of the OGC Web Service Interface Specifications for Coverage and Feature Data within OPeNDAP

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Presented at the AGU Fall Meeting 2008


The OPeNDAP Data Access Protocol has seen widespread adoption within the science community. OPeNDAP servers are currently deployed by individual investigators, academic institutions, and at national and international data repositories to provide distributed data access for their respective user communities. Many of these data providers anticipate that there will be significant demand for data access by applications using the suite of OGC web service specifications. Supporting multiple data access protocols can be expensive, both in the initial acquisition and deployment cost for the software components as well as for the potentially redundant maintenance and security costs required when supporting multiple server implementations operationally. To provide a cost-effective solution for these data providers OPeNDAP is developing extensions to its data access protocol to enable the use of semantic web technologies for data and metadata transformations, and extensions to its server architecture to support request and response operations simultaneously for multiple data access protocols.

The OGC Web Coverage Service Interface Specification is the initial data access protocol to be layered onto the OPeNDAP server for this multi-protocol support. Supporting data access through the OGC service interfaces comprises operations that are both mechanical and semantic. The OPeNDAP server architecture (Hyrax) uses a Lightweight Front-End Server (OLFS) that is responsible for interacting with the requesting client application. The OLFS is extensible and in this project has been extended to support the OGC web service interface specifications. Coupled with the OLFS the Hyrax architecture uses a Back-End Server (BES) to provide data access, processing, and response generation that are then returned through the OLFS to the requesting client. Similar to the OLFS, the BES is extensible and for this project has been extended to support various mechanical actions required in support of the OGC service’s request and response interface specification. In addition to the simpler, mechanical aspects required to support these multiple protocols, semantic operations are required in order to interpret request elements and for constructing well-formed OGC responses. To support these semantic operations we’ve developed ontological representations of the OGC, OPeNDAP, and NetCDF/CF data models, and the relationships between those models. The OLFS has been extended to support XSLT operations transforming OPeNDAP’s XML data descriptor (DDX) to a Resource Description Framework (RDF) representation. Modules executing during server initialization ingest the RDF representations and use the ontologies to crosswalk the metadata elements between the protocols.


DateCreated ByLink
April 11, 2013
Patrick WestDownload

Related Projects:

Description: Tasks for various TWC projects related to data access and the OPeNDAP software products.

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.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts: eScience