An efficient filtering technique for super-graph query processing

Recently, a lot of applications depend on data modeled by graphs. Efficient query processing over graph databases serves these applications. Having a graph query q, super-graph query processing finds all the graphs g in a database of graphs D where g is contained in q (gsubeq). Because graph databases contain a lot of graphs and because sub-graph isomorphic tests are NP-complete, an indexing-based technique should be adopted. In this paper we propose an efficient technique for processing supergraph queries. The technique consists of an index called eIndex and a query processing algorithm. Given a query graph, the database is filtered to generate candidate graphs. While filtering, the proposed technique takes into consideration the full structure of the database graphs besides considering the frequent fragments. Through polynomial time algorithms, the proposed technique reduces the subgraph isomorphism tests required for query processing and hence the total processing time is reduced. The results show that building eIndex takes much less time and space than other methods.