Abstract
The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domains.
Highlights
Many applications require detailed knowledge about environmental conditions to incorporate in different decision-making tasks
One step beyond the definition of simple vocabularies and terminologies is the incorporation of complex constraints and knowledge rules that may be expressed in the Semantic Web Rule Language (SWRL) [49], a rule language that combines the representation power of Ontology Web Language (OWL) with the reasoning power of RuleML
It is estimated that the combination of those synopses with geospatial approximate representations within geospatial data structures would enable the construction of efficient geospatial approximate query processing engines, which would support integrated exploratory analytics over vector and raster datasets
Summary
Many applications require detailed knowledge about environmental conditions to incorporate in different decision-making tasks. Sci. 2020, 10, 856 enable IT practitioners to discover and access them in an effective and efficient manner As it is illustrated, the models, standards and technologies involved in the construction of the geospatial and environmental data infrastructures used by scientific and engineering applications, are not aligned with the semantic technologies on which general purpose open data infrastructures are based. Despite the existence of a geospatial extension of SPARQL [12] that supports only geospatial vector feature data, in general, the complexity of the spatio-temporal structures and semantics found in environmental data sources is not supported by currently available conventional open data infrastructures.
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