2 research outputs found
Adaptive Low-level Storage of Very Large Knowledge Graphs
The increasing availability and usage of Knowledge Graphs (KGs) on the Web
calls for scalable and general-purpose solutions to store this type of data
structures. We propose Trident, a novel storage architecture for very large KGs
on centralized systems. Trident uses several interlinked data structures to
provide fast access to nodes and edges, with the physical storage changing
depending on the topology of the graph to reduce the memory footprint. In
contrast to single architectures designed for single tasks, our approach offers
an interface with few low-level and general-purpose primitives that can be used
to implement tasks like SPARQL query answering, reasoning, or graph analytics.
Our experiments show that Trident can handle graphs with 10^11 edges using
inexpensive hardware, delivering competitive performance on multiple workloads.Comment: Accepted WWW 202