# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 4 Data Acquisition and Integration
### Smart Environmental Data Integration  
The main challenge to be faced to achieve the effective development of data acquisition systems for environmental applications is the very high heterogeneity of the different sources of data described in the previous subsection. The specification and use of standard models and interfaces ease the data integration processes, and therefore, efforts in that direction have been done in the environmental domain, with the O&M data model [11], the SensorML language [72] and the transactional part of the sensor observation service (SOS) [73], among others. The same problem arises also in IoT [74] and Smart City [75] applications. Therefore, similar efforts have been undertaken in these areas to reach standard interfaces and models. Representative examples are the open messaging interface (O-MI) and open data format (O-DF), proposed by The Open Group, the MQ Telemetry Transport (MQTT), which is a standard of both OASIS and ISO, and the NGSI RESTful API of the FIWARE Context Broker. The simplest data integration solutions provide only syntactic integration [76] of the data sources, enabling uniform access to heterogeneous model paradigms and formats. Thus, it is common to generate RDF data from stored relational data, since many datasets are already available in relational format. This may be done to query directly relational datasets with SPARQL or to implement ETL processes that load relational data into RDF data stores. To ease these tasks, W3C has defined a direct mapping between relational data to RDF [77], where each tuple of each relation is automatically mapped to a set of RDF triples using the database catalog to obtain names for classes, properties, and individuals. In case a target RDF vocabulary is already available, the direct mapping cannot be used. For those cases, Relational Database to RDF Mapping Language (R2 RML) [78] enables the definition of highly customized RDF views over relational data through the specification of user defined mappings. The resolution of semantic conflicts is the responsibility of the programmer of either ETL processes in data warehouse architectures [79] or data access wrappers in federated architectures [80] implemented with the classical mediator/wrapper pattern [81]. The semantic integration of data sources has already been identified as a challenging key functionality to be supported in advanced data infrastructures [82], including those in the scope of environmental data [83], and those in the scope of IoT [74]. To achieve this, three main tasks have to be undertaken. First, top-level general and standard ontologies must be developed to provide spaces for the integration process, and data sources must be semantically enriched. Next, semantic mappings between data source and general ontologies must be discovered, to construct the glue knowledge that enables the model based integration process [84]. Finally, reasoning mechanisms must be implementedover the integrated knowledge to solve global data access. During the last decade, the research on semantic technologies applied to e-science mostly centered on the development and alignment of ontologies and on the development of frameworks and use cases [85]. Those solutions have to be now leveraged to construct modern semantic data integration approaches. Regarding geospatial data, according to [86], new semantic data integration solutions should pay attention to the special characteristics of spatial data both during data representation and integration. Various semantic data integration solutions have already been proposed in various environmental application domains, including crop modeling in agriculture [87], solar terrestrial observation [88], water management [51] and geology [89]. Approaches defined from a more general purpose perspective have also been proposed in the scope of OGC data models and services, focusing on geospatial data [90] and more specifically on environmental observation data [45; 91].