DREAM in Action: A Distributed and Adaptive RDF System on the Cloud.

RDF and SPARQL query language are gaining wide popularity and acceptance. This demonstration paper presents DREAM, a hybrid RDF system, which combines the advantages and averts the disadvantages of the centralized and distributed RDF schemes. In particular, DREAM avoids partitioning RDF datasets and reversely partitions SPARQL queries. By not partitioning datasets, DREAM offers a general paradigm for different types of pattern matching queries and entirely precludes intermediate data shuffling (only auxiliary data are shuffled). By partitioning only queries, DREAM suggests an adaptive scheme, which runs queries on different numbers of machines depending on their complexities. DREAM achieves these goals and significantly outperforms related systems via employing a novel graph-based, rule-oriented query planner and a new cost model. This paper proposes demonstrating DREAM live over the cloud using a friendly graphical user interface (GUI). The GUI allows participants to execute and visualize pre-defined and user-defined (which can be written by participants on-the-fly) SPARQL queries over various real-world and synthetic RDF datasets. Furthermore, participants can empirically compare and contrast DREAM against three state-of-the-art RDF systems.