Transforming Big Data Into Knowledge Using Semantic Stream Processing Technology: Challenges and Early Progress

Abstract

In the Big Data era, the volume of streaming data generated from heterogeneous data sources such as social media, smart devices or sensor networks is overwhelming but enables human users and applications to better understand the world around us. Understanding the context, situation or the environment where the data has been produced is a challenging task due the dynamic and fast nature of the streaming data. Modelling the big data using RDF model and applying continuous SPARQL queries over it using Semantic Web tools helps create machine-understandable meanings and valuable insights of the underlying data. This paper provides an overview of the Big Data theoretical foundation, challenges and stages that need to be taken into consideration when building a Big Data pipeline architecture. Also, the research efforts that have been made in the past years using Semantic Stream Processing techniques are described.