Efficient Processing of Large RDF Streams Using Memory Management Algorithms

As more RDF streaming applications are being developed, there is a growing need for an efficient mechanism for storing and performing inference over these streams. In this poster, we present a tool that stores these streams in a unified model by combining memory and disk based mechanisms. We explore various memory management algorithms and disk-persistence strategies to optimize query performance. Our unified model produces an optimized query execution and inference performance for RDF streams that benefit from the advantages of using both, memory and disk.