Finalized:Monday, April 2, 2018
Author(s):Fredericks, J. and Botts, M.
Sensor technologies and capabilities have an effect on observational data quality. Typically, data management begins, at best, when a data manager obtains the data and needs to describe it sufficiently to data consumers. Often, the sensing methods are not adequately described and the data manager does not know the appropriate questions to ask or where to direct questions about sensors, their configuration, and the deployment. Consequently, knowledge often remains buried in sensor manuals and field operator logs. Thus, most metadata requirements have been simplified to accommodate this gap in knowledge.
When information is captured where it is best understood and tools are created to easily capture this knowledge, machine-actionable descriptions can be provided to adequately describe the processes taken in generating observations.
The information can be associated with the data and thus be accessible, discoverable and used in data quality controlby data providers and in data quality assessment by the data consumers.
Here, we define actors and actions to promote role-based creation of fully-described, standards-based documents. These documents can be created in Sensor ML (OGC SWE) that includes links to resolvable term definitions (W3C Semantic Web), enabling the creation of associated mappings and ontologies to extend and resolve the meaning of each term.
Fredericks, J. and Botts, M. (2018). Promoting the capture of sensor data provenance: a role-based approach to enable data quality assessment, sensor management and interoperability. Open Geospatial Data, Software and Standards (2018) 3:3. DOI: https://doi.org/10.1186/s40965-018-0048-5This material is based upon work supported by the National Science Foundation under Grant No. 1541008. Opinions, findings, conclusions or recommendations expressed are those of the authors and do not reflect the views of the NSF.