Implementing Graph Pattern Mining for Big Data in the Cloud

With the increasing popularity of various social networking sites, there is an explosive growth in data associated with these, so mining big data has become an important problem in the graph pattern mining research area. Graph mining helps to explore the patterns from networks or databases. Till now various graph mining techniques exist for mining frequent patterns for a graph database which contains relatively small sized graphs. But with the rapid arrival of the era of big data, traditional graph mining approaches have been unable to meet large data analysis needs. In this context, this paper proposes an adaptation to the big graph data mining approach especially in the field of social networks. The proposed approach is based on Hadoop plateform, and improves the efficiency by processing big data in distributed fashion. Again the proposed approach can be adapted to cloud environment which has the merits – load balancing, scalability and efficiency. Experiments have been conducted with real Facebook data set. The approach can be also adapted to dataset larger than experimented data. DOI: 10.17762/ijritcc2321-8169.150514