Abstract:
In recent years, graph has become a powerful
tool for representing and modeling objects and their
relationships in various application domains such as
protein interactions, chemical compounds, social
networks, XML documents and so on. The volume of
graph data increases rapidly and the graph database
becomes an essential role to store graph data.
However, the performance of query processing on
graph databases is still inadequate due to the high
complexity of processing graph data. As a result, it is
important to develop efficient indexing structure for
query processing on the graph databases. In this paper,
we propose both algorithms for graph indexing and
subgraph isomorphism query for subgraph query
processing in the graph database. We also propose a
proficient index structure (AdE) to support both
algorithms to quickly index and identify the isomorphic
graphs for the given query graph. Like canonical code,
AdE checks whether the query graph is a subgraph
isomorphic to the database graph. Our proposed index
structure significantly reduces the computational time
complexity compared to the DGIndex structure.
