A Semantic Based Framework for the Purpose of Big Data Integration
David Alfred Ostrowski, Mira Kim · 2017
One of the most substantial opportunities in Big Data is to integrate disparate data sources across the enterprise. To realize this goal it can be valuable to leverage environments developed for high speed parallel processing as well as toolkits supporting the development and maintenance of semantic information. In support of this approach a proposed framework and methodology is presented to utilize an ontology-based data integration strategy. Our approach supports a rule-based translation to generate new ontology versions within a fast prototyping environment leveraging the Jena API within the context of the Apache Spark environment.