# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## Data and Metadata Representation and Storage
### Geospatial Smart Representations
The W3C PROV Ontology (PROV-O) [41], at a high level of abstraction, represents how different agents may be involved in the generation of entities through the execution of different activities. Some research activities already report the use of PROV-O in the scope of environmental data management. Thus, for example, in [42] PROV-O is used to design an information model that represents the provenance information related to a climate assessment report, in the scope of climate change analysis. In [43], the gaps between ISO 19115 lineage model and PROV-O are analyzed to undertake a PROV-O extension with ISO 19115 based semantics, enabling this way PROV-O to be effectively used in geospatial application domains. Beyond the above and other general-purpose top-level ontologies, more specific ontologies that may be used as top-level models and vocabularies for environmental applications have already been designed. One such ontology is the semantic sensor network ontology (SSN) [13; 44], which includes elements to describe sensing systems and their observations. The lightweight core of SSN is the sensor observation, sample, and actuator (SOSA) ontology. This ontology may be used to represent datasets that follow simple OGC O&M models, but it may be extended to support its whole expressiveness [45]. Additionally, SOSA enables the representation of complex sensor systems (see Section 4) and provides concepts to model the actuation mechanisms that are needed in IoT application domains. Many vocabularies have been designed in the scope of different geoscience application domains. A first example is the Semantic Web for Earth and Environmental Terminology (SWEET) [14], which is a collection of ontologies constructed in NASA with the broad scope of Earth science. It consists of 11 primary ontologies that contain a total of several thousand concepts related to natural phenomena, human activities, physical processes and properties, units, etc. Efforts to design and integrate vocabularies in more specific application domains have been developed. Some examples may be found in meteorology ([[http://cfconventions.org/index.html](http://cfconventions.org/index.html)]([http://cfconventions.org/index.html](http://cfconventions.org/index.html))), oceanography [46], environment ([[https://www.eionet.europa.eu/gemet/](https://www.eionet.europa.eu/gemet/)]([https://www.eionet.europa.eu/gemet/](https://www.eionet.europa.eu/gemet/))), geology [47] and mineral exploration [48]. 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 OWL with the reasoning power of RuleML. Such knowledge may next be used to perform inferences to achieve for example complex data validation [50] and advanced decision support tools [51]. To summarize, the smart representation of geospatial vector features in RDF is currently supported by the GeoSPARQL ontology [12]. However, such ontology is not designed to support efficient spatial and spatio-temporal raster coverages. Only the metadata of those coverages may be efficiently managed [52]. An initial attempt to support array data representation and querying with semantic technologies is reported in [53], where arrays are represented with a new data type as RDF collections of numbers. This is somehow similar to the use of raster data types in current spatial DBMSs. The main problem of this approach is that it assumes a complex nested model where arrays are represented as data elements of the RDF model. Therefore, to be able to manipulate array elements, the user must use specific array operators of the relevant data type. Contrary to the above approach, the multidimensional model defined in [54] supports vector feature collections and spatial and spatio-temporal raster coverages with a unique and simple primitive data structure (a data cube called MappingSet).