Freshening up while Staying Fast: Towards Hybrid SPARQL Queries

Querying over cached indexes of Linked Data often suffers from stale or missing results due to infrequent updates and partial coverage of sources. Conversely, live decentralised approaches offer fresh results directly from the Web, but exhibit slow response times due to accessing numerous remote sources at runtime. We thus propose a hybrid query approach that improves upon both paradigms, offering fresher results from a broader range of sources than Linked Data caches while offering faster results than live querying. Our hybrid query engine takes a cached and live query engine as black boxes, where a hybrid query planner splits an input query and delegates the appropriate sub-queries to each interface. In this paper, we discuss query planning alternatives and their main strengths and weaknesses. We also present coherence measures to quantify the coverage and freshness for cached indexes of Linked Data, and show how these measures can be used for hybrid query planning to optimise the trade-off between fresh results and fast runtimes.