The amount of Linked Data is increasing steadily. Optimized top-down Linked Data query processing based on complete knowledge about all sources, bottom-up processing based on run-time discovery of sources as well as a mixed strategy that combines them have been proposed. A particular problem with Linked Data processing is that the heterogeneity of the sources and access options lead to varying input latency, rendering the application of blocking join operators infeasible. Previous work partially address this by proposing a non-blocking iterator-based operator and another one based on symmetric-hash join. Here, we propose detailed cost models for these two operators to systematically compare them, and to allow for query optimization. Further, we propose a novel operator called the Symmetric Index Hash Join to address one open problem of Linked Data query processing: to query not only remote, but also local Linked Data. We perform experiments on real-world datasets to compare our approach against the iterator-based baseline, and create a synthetic dataset to more systematically analyze the impacts of the individual components captured by the proposed cost models. Linked Data Query Processing. As usual [3, 6] , we conceive Linked Data sources as interlinked sets of RDF triples [5]: Definition 1. A source d is a set of RDF triples s d , p d , o d ∈ T d where s d is called the subject, p d the predicate and o d the object. There is a function ID
Ladwig et al. REF propose an operator called SIHJoin, a symmetric index hash join that addresses the problem of Linked Data query processing not only remotely but also locally.
SIHJoin: querying remote and local linked data
ESWC
Computer Science