Data Parallelism : A New Approach in Prediction Systems - Borole, KB; Rajput, SD (International Journal of Computer Sciences and Engineering, 2018)
The day-by-day growing data can compromise the performance of the prediction system, because its obvious that the growing data will require more storage and the system will also consume more time for its processing. In prediction system, testing is part where time is consumed. If the entire data is given to the test model, it will run for the entire input size, and becomes time consuming. For this effective reduction strategy for processing time of testing must be introduced. To reduce this processing time introducing parallelism concept can help. The framework used here is based on fork join pool. In this the input size is divided into parts which are small enough to be processed and then the divided parts are given for testing. Thus reducing the time consumed in testing, and making it better than the other system.