MapReduce-Based Algorithms for Managing Big RDF Graphs: State-of-the-Art Analysis, Paradigms, and Future Directions

Big RDF (Resource Description Framework) graphs, which populate the emerging Semantic Web, are the core data structure of the so-called Big Web Data, the "natural" transposition of Big Data on the Web. Managing big RDF graphs is gaining momentum, essentially due to the fact that this task represents the "baseline operation" of fortunate Web big data analytics. Here, it is required to access, manage and process large-scale, million-node (big) RDF graphs, thus dealing with severe spatio-temporal complexity challenges. A possible solution to this problem is represented by the so-called MapReduce-model-based algorithms for managing big RDF graphs, which try to exploit the computational power offered by the MapReduce processing model in order to tame the complexity above. In this so-depicted scientific context, this paper provides a critical survey on MapReduce-based algorithms for managing big RDF graphs, with analysis of state-of-the-art proposals, paradigms and trends, along with a comprehensive overview of future research trends in the investigated scientific area.