TripleRush: a fast and scalable triple store

TripleRush is a parallel in-memory triple store designed to address the need for efficient graph stores that answer queries over large-scale graph data fast. To that end it leverages a novel, graph-based architecture. Specifically, TripleRush is built on our parallel and distributed graph processing framework Signal/Collect. The index structure is represented as a graph where each index vertex corresponds to a triple pattern. Partially matched copies of a query are routed in parallel along different paths of this index structure. We show experimentally that TripleRush takes less than a third of the time to answer queries compared to the fastest of three state-of-the-art triple stores, when measuring time as the geometric mean of all queries for two benchmarks. On individual queries, TripleRush is up to three orders of magnitude faster than other triple stores

Random-walk triplerush: asynchronous graph querying and sampling

Most Semantic Web applications rely on querying graphs, typically by using SPARQL with a triple store. Increasingly, applications also analyze properties of the graph structure to compute statistical inferences. The current Semantic Web infrastructure, however, does not efficiently support such operations. Hence, developers have to painstakingly retrieve the relevant data for statistical post-processing. In this paper we propose to rethink query execution in a triple store as a highly parallelized asynchronous graph exploration on an active index data structure. This approach also allows to integrate SPARQL-querying with the sampling of graph properties. To evaluate this architecture we implemented Random Walk TripleRush, which is built on a distributed graph processing system and operates by routing query and path descriptions through a novel active index data structure. In experiments we find that our architecture can be used to build a competitive distributed graph store. It can often return first results quickly, thanks to its asynchronous architecture. We show that our architecture supports the execution of various types of random walks with restarts that sample interesting graph properties. We also evaluate the scalability and show that the architecture supports fast answer times even on a dataset with more than a billion triples

Scalable Graph Processing With SIGNAL/COLLECT

Our ability to process large amounts of data and the size and number of data sets are growing at an incredible pace. This development presents us with the opportunity to build systems that perform complex analyses of increasingly dense networks of data. These opportunities include computing recommendations, analysing social networks, finding patterns in transaction networks, scheduling tasks, or inferencing probabilistic models. Many of these tasks involve processing data that has a natural graph representation. Whilst the opportunities are there in the form of access to processing resources and data sets, the way we write software has largely not caught up. Many use MapReduce for scalable processing, but this abstraction has shortcomings with regard to processing graph structured data, especially with iterative and asynchronous processing. This thesis introduces the SIGNAL/COLLECT programming model and framework for efficient parallel and distributed large-scale graph processing. We show that this abstraction captures the essence of many algorithms on graphs in a concise and elegant way. Beyond that, we also show implementations of two complex systems built on SIGNAL/COLLECT: The first system is TripleRush, a distributed in-memory triple store with a novel architecture. The second system is foxPSL, a distributed proba- bilistic inferencing system. Our evaluations show that the SIGNAL/COLLECT framework can efficiently execute simple graph algorithms such as PageRank and that the two complex systems also have competitive performance relative to the respective state-of-the-art. For this reason we believe that SIGNAL/COLLECT is more generally suitable for designing scalable dynamic and complex systems that process large networks of data