A SURVEY ON MAPREDUCE USING FREQUENT SUBGRAPH MINING

The big data is a huge amount of data, when compared to other datasets. The large number of data is stored in memory, the stored data will be retrieved (wanted data) in that time we are facing the many number of problems. And also the problems resolved means, in problem solving operation using the data mining process. In data mining, large amount of data, the wanted data only extracted from big data, ie, stored large amount of data. Some storage area only using the data mining concepts and also using the map reduce concepts. The map reduce concepts are mainly focused on the frequent set of items, the frequent path finding and then the map reducing process is executed. In map reduce process, having two type of functionalities. They are mapping and reducing functions used in map reduce process. In map reduce function of operation, the large amount of graph data will be divided into sub graphs, divided sub graphs are merged. For the use of finding the frequent set of path, time is reduced for retrieving the wanted set of data.