Distributed Efficient Provenance-Aware Regular Path Queries on Large RDF Graphs

Abstract

With the proliferation of knowledge graphs, massive RDF graphs have been published on the Web. As an essential type of queries for RDF graphs, Regular Path Queries (RPQs) have been attracting increasing research efforts. However, the existing query processing approaches mainly focus on the standard semantics of RPQs, which cannot provide provenance of the answer sets. We propose dProvRPQ that is a distributed approach to evaluating provenance-aware RPQs over big RDF graphs. Our Pregel-based method employs Glushkov automata to keep track of matching processes of RPQs in parallel. Meanwhile, four optimization strategies are devised, including edge filtering, candidate states, message compression, and message selection, which can reduce the intermediate results of the basic dProvRPQ algorithm dramatically and overcome the counting-paths problem to some extent. The proposed algorithms are verified by extensive experiments on both synthetic and real-world datasets, which show that our approach can efficiently answer the provenance-aware RPQs over large RDF graphs.