LARPQ evaluates two-way regular path queries by expressing the simultaneous traversal of graph and query automaton as Boolean linear algebra, with a correctness proof and a mixed but often favorable performance evaluation.
Accelerating Regular Path Queries over Graph Database with Processing-in-Memory
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abstract
Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall. Emerging processing-in-memory (PIM) technologies offer a promising solution to dispatch and execute path matching tasks in parallel within PIM modules. We present Moctopus, a PIM-based data management system for graph databases that supports efficient batch RPQs and graph updates. Moctopus employs a PIM-friendly dynamic graph partitioning algorithm, which tackles graph skewness and preserves graph locality with low overhead for RPQ processing. Moctopus enables efficient graph update by amortizing the host CPU's update overhead to PIM modules. Evaluation of Moctopus demonstrates superiority over the state-of-the-art traditional graph database.
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Single-Source Regular Path Querying in Terms of Linear Algebra
LARPQ evaluates two-way regular path queries by expressing the simultaneous traversal of graph and query automaton as Boolean linear algebra, with a correctness proof and a mixed but often favorable performance evaluation.