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 ∈ 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.
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HyGraph: a subgraph isomorphism algorithm for efficiently querying big graph databases
The big graph database provides strong modeling capabilities and efficient querying for complex applications. Subgraph isomorphism which finds exact matches of a query graph in the database efficiently, is a challenging problem. Current subgraph isomorphism approaches mostly are based on the pruning strategy proposed by Ullmann. These techniques have two significant drawbacks- first, they are unable to efficiently handle complex queries, and second, their implementations need the large indexes that require large memory resources. In this paper, we describe a new subgraph isomorphism approach, the HyGraph algorithm, that is efficient both in querying and with memory requirements for index creation. We compare the HyGraph algorithm with two popular existing approaches, GraphQL and Cypher using complexity measures and experimentally using three big graph data sets—(1) a country-level population database, (2) a simulated bank database, and (3) a publicly available World Cup big graph database. It is shown that the HyGraph solution performs significantly better (or equally) than competing algorithms for the query operations on these big databases, making it an excellent candidate for subgraph isomorphism queries in real scenarios.
more » « less- Award ID(s):
- 1901150
- PAR ID:
- 10543323
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Big Data
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2196-1115
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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