# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 6 Wrap-up Discussion on New Research Challenges
#### 2.2.3 Challenge 4: Geospatial to RDF Mappings
In particular, the federated query engine should leverage vector data processing capabilities present in spatial DBMSs and raster data processing capabilities provided by array data managers [29; 30]. Although semantic federated querying is already provided by SPARQL [19] implementations, they do not support integrated geospatial querying of vector and raster data. Some available GeoSPARQL [12] implementations may federate various vector data sources, however, they do not leverage different data processing capabilities of heterogeneous geospatial data sources. Finally, SciSPARQL [53] supports raster querying, but does not allow the federation of various data sources. Challenge 8: Keyword-Based Search over Geospatial Data Sources  
Keyword-based searching is a key piece of functionality required to implement data discovering, which is a key process to bring datasets closer to IT practitioners, and therefore to citizen applications. To achieve really effective search, in terms of the precision and recall of the system response, the implementations should enable the searching over both conventional and geospatial metadata elements and over both text and numeric properties. Besides, fuzzy spatial and temporal relationships between data elements should be incorporated in a simple and usable keyword-based query language. The Full Text Search (FTS) capabilities of SPARQL may only be used to query text properties, however, the user must have knowledge about the schema of the dataset. The OGC CSW standard [15] enables dataset discovering using keyword-based and spatial search over geospatial metadata. However, it may not be used to query the dataset contents with keywords. Similar functionality is also available in open data portals that use the GeoDCAT-AP data model. Relational keyword-based search approaches support the query over text properties [106] of relational datasets, however, in general they do not leverage the existence of geospatial metadata and they do not support either the querying of numeric properties with keywords, using fuzzy linguistic values, nor the incorporation of spatial and temporal relationships such as \"after\", \"nearby\", \"inside\", etc. Finally, an initial approach towards generic keyword-based search over environmental datasets, called Keywordterm, has already been described [107]. However, much work is still required related to this challenging problem to achieve solutions with good performance, both in terms of effectiveness (precision and recall of the result) and efficiency (size of the indexing structures, index construction and update times and search response time). Challenge 9: Interactive Exploratory Analysis of Very Large Geospatial Datasets  
Once the data has been discovered and before it may be incorporated into complex decision-making mechanisms, it has to be analytically explored. Exploring geospatial and environmental data is usually done through map based graphical user interfaces. However, required system functionality does not restrict to simple map rendering, since statistical analysis has to be evaluated during data exploration. Supported data types must include both vector features and raster coverages, but two other important requirements related to the system performance must be included. In particular, the response time must be short enough to guarantee interactive exploration of the analysis results. To achieve that, approximate results that have some controlled error may be generated. Current OGC WMS service implementations support both vector and raster data, but only partially since they only provide map rendering and do not support user-defined statistical analysis. Besides, no control over the resulting error is given to the user. Response times may be improved, despite the good performance achieved when dataset (vector or raster) tiling and map overviews are used.