Indexing Frequent Subgraphs in Large graph Database using Parallelization

Plenty of structural patterns in real world have been represented as graph like molecules, chemical compounds, social network, road network etc. Mining this graph for extracting some useful information is of special interest and has many applications. The application includes drug discovery, compound synthesis, anomaly detection in network, social network analysis for finding groups etc. One of the most interesting problems in graph mining is graph containment problem. In graph containment problem, given a query graph q, it is asked to find all graph in given graph dataset containing this query (query graph as subgraph). This means finding all graph which is isomorphic to query graph. As in real world there is vast number of graph in graph dataset so this task of subgraph isomorphism test become tedious, complex, time and space consuming. So it is necessary to create an index of graphs present in dataset for cost efficient query processing. In this paper we proposed a time efficient graph indexing technique using discriminative frequent subgraph as indexing feature for molecular datasets using parallel approach. We proposed a method which will find frequent subgraphs using better pruning capability and executed in multithreaded environment in parallel manner. Our experimental studies conceal that parallelization method for graph indexing which has a condensed index structure, achieves an order of degree better performance in index construction, and significantly, outperforms state-of-the-art graph based indexing methods