The key objective of SALTED is to add value to existing datasets and data-streams by enriching them through the application of the principles of linked-data, semantics and Artificial Intelligence. These datasets and data-streams come from heterogenous data sources such as Internet of Things (IoT) deployments, Open Data Portals and Social Media, and are harmonized towards a standard information model, i.e. NGSI-LD, targeting the so-essential interoperability.
The Data Enrichment Toolchain (DET) architecture designed and developed comprises different microservices that, on the whole, address the challenges presented along the achievement of the main goal of the project. The principal microservices are:
• data discovery, i.e., the ability to discover and request the collection of sets and streams of data;
• data formatting, i.e., the transformation of raw data into well-formed and structured sets of data accordingly to data models described in terms of NGSI-LD;
• data curation, i.e., the identification (and potential correction) of data that do not reflect the expected quality (outliers, errors in values and the like);
• data linkage, i.e., the ability to relate different datasets accordingly to a well established definition of relationships;
• data enrichment, i.e., the ability to understand and frame the data structures according to situations and contexts and the definition of functions that exploit this contextualization.