Abstract
A subgraph query q that finds as output all its subgraph-isomorphic embeddings from a data graph g has been core to modern declarative querying in large graphs. In this paper, we address subgraph queries with the availability of query workload information, W = {w1,..., wn}, where wi in W is a previously issued query with all its subgraph-isomorphic embeddings cached beforehand. We introduce a workload-aware subgraph querying framework, WaSQ, that leverages query workload for subgraph query rewriting, search plan refinement, partial results reusing, and false positive filtering towards facilitating the whole subgraph querying process. Experimental studies in real-world graphs demonstrate that WaSQ achieves significant and consistent performance gains in comparison with state-of-the-art, workload-oblivious solutions for large-scale subgraph querying.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
