Heterogeneous Map Reduce Scheduling Using First Order Logic

The objective of the research is to implement big data for a Map Reduce technique that helps to process queries from the user in an efficient way. The main theme of the map reduce is to optimize the time factor. First Data collection of the documents, secondly mapping to the systems, third tries to do the shuffling and at least reduce the information. Here the same Map Reduce is used to process large amount of data through the servers in a parallel and distributed manner. In the research work, this is the first time, The First order logic method is applied for map reduces scheduling for heterogeneous data. The results are obtained in an optimized way through the above said methodology. The paper concludes that the resources are utilized very efficiently and work has done in a faster mode and with greater accuracy.