ABSTRACT. In the last years, huge RDF graphs with trillions of triples were created. To be able to process this huge amount of data, scalable RDF stores are used, in which graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. The main challenges to be investigated for the development of such RDF stores in the cloud are: (i) strategies for data placement over compute and storage nodes, (ii) strategies for distributed query processing, and (iii) strategies for handling failure of compute and storage nodes. In this manuscript, we give an overview of how these challenges are addressed by scalable RDF stores in the cloud.

ABSTRACT. This tutorial gives an overview of current methods for per- forming reasoning on very large knowledge bases. After a brief introduction of the problem and related technologies, the tutorial will describe the challenges in this area and discuss the state-of-the-art for reason- ing on very large inputs with a particular focus on the strengths and weaknesses of current approaches.