XRel: a path-based approach to storage and retrieval of XML documents using relational databases
ACM Transactions on Internet Technology (TOIT)
Modern Information Retrieval
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Mining protein family specific residue packing patterns from protein structure graphs
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Closure-Tree: An Index Structure for Graph Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
On synopses for distinct-value estimation under multiset operations
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Fg-index: towards verification-free query processing on graph databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Towards graph containment search and indexing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A novel spectral coding in a large graph database
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Efficient and scalable statistics gathering for large databases in Oracle 11g
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Dependable cardinality forecasts for XQuery
Proceedings of the VLDB Endowment
A novel approach for efficient supergraph query processing on graph databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
TALE: A Tool for Approximate Large Graph Matching
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Community mining from multi-relational networks
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions, XML documents and multimedia databases. To be able to effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. Supergraph query is an important type of graph queries which has many practical applications. Given a graph database D, the answer set of a supergraph query q is computed by retrieving all graphs in D which are fully contained in q. A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems. For example, subgraph isomorphism is known to be NP-complete. Clearly, the success of any graph database application is directly dependent on the efficiency of the graph indexing and query processing mechanisms. In this paper, we study the problem of using the relational infrastructure to achieve an efficient evaluation of supergraph queries. We rely on an effective and efficient layer of features-based summary structures, called graph features knowledge, to reduce the required number of pair-wise graph comparisons and boost the efficiency of query processing. Finally, we conduct an extensive set of experiments on real and synthetic data sets to demonstrate the efficiency and the scalability of our approach.