Data Quality Screening Service
- Sunday, 01 December 2013
A report describes the Data Quality Screening Service (DQSS), which is designed to help automate the filtering of remote sensing data on behalf of science users. Whereas this process often involves much research through quality documents followed by laborious coding, the DQSS is a Web Service that provides data users with data pre-filtered to their particular criteria, while at the same time guiding the user with filtering recommendations of the cognizant data experts.
The DQSS design is based on a formal semantic Web ontology that describes data fields and the quality fields for applying quality control within a data product. The accompanying code base handles several remote sensing datasets and quality control schemes for data products stored in Hierarchical Data Format (HDF), a common format for NASA remote sensing data. Together, the ontology and code support a variety of quality control schemes through the implementation of the Boolean expression with simple, reusable conditional expressions as operands.
Additional datasets are added to the DQSS simply by registering instances in the ontology if they follow a quality scheme that is already modeled in the ontology. New quality schemes are added by extending the ontology and adding code for each new scheme.
This work was done by Richard Strub, Christopher Lynnes, Thomas Hearty, and Young-In Won of Goddard Space Flight Center; and Peter Fox and Stephan Zednik of Rensselaer Polytechnic Institute. GSC-16227-1
This Brief includes a Technical Support Package (TSP).
Data Quality Screening Service (reference GSC-16227-1) is currently available for download from the TSP library.
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