Query Time Optimization Using Hungarian Algorithm

The huge amount of data that is committed to the databases, takes more time, than the time that is estimated which further creates trouble for database transactions. To untighten this process, data needs to be clustered based on common attributes, to commit data faster. Databases like Hadoop have structures like MapReduce, which are incompetent to handle the process alone. As a result, the accumulation of these transactions eventually slows down the process as well as adversely affects the performance of the application that creates havoc at the user end. The proposed system puts forth the idea of decomposing this data to hyper graphs based on correlating features between them. Furthermore, the use of Hungarian and Genetic Algorithm, achieves optimized transaction time.