RDF-Gen: generating RDF triples from big data sources

Transforming disparate and heterogeneous data sources that provide large volumes of data in high velocity into a common form allows integrated and enriched views on data and thus provides further opportunities to advance the effectiveness and accuracy of data analysis and prediction tasks. This paper presents the RDF-Gen approach for transforming data provided by archival and streaming data sources, provided in various formats, into RDF triples, according to a set of ontological specifications. RDF-Gen introduces a generic mechanism which supports the transformation of data efficiently (i.e., with high throughput and low latency), even in cases where the velocity of data presents high peaks, offering facilities for discovering associations between data from different sources, and supporting transformation of modular data sets. This paper presents a parallel implementation of RDF-Gen, also presenting data transformation workflows that allow variations incorporating RDF-Gen instances, adjusting to the needs of data sources, application areas and performance requirements. RDF-Gen is experimentally evaluated against state of the art, in both archival and streaming settings: Experimental results show RDF-Gen efficiency and highlight key contributions.