• 1Jyotsna B. Jagdale

As data is growing so fast every day, analysis of big data is a big problem for traditional analysis technique. Data generated from various resources is huge in volume and highly unstructured in nature, it is thus important to structure the data and leverage its actual potential. This requires a need for new techniques and frameworks to aid humans in automatically and intelligently analyzing large data sets to acquire useful information. Here, propose a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark server. The PRF algorithm is optimized based on a hybrid approach combination of data-parallel and task-parallel optimization. The algorithm incrementally estimates the accuracy for classifying the data streams, which is priors to the parallelization process in order to reduce the training time and prediction process using random sampling and filtering approach, that improves the dynamic-data allocation and task-scheduling mechanism in a cloud platform.

Apache Spark, Big Data, Cloud Computing, Data Parallel, Random Forest, Task Parallel .

"Random Forest Parallel Approach for Big Data Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.328-333, July-2018, Available :http://www.jetir.org/papers/JETIR1807650.pdf

"Random Forest Parallel Approach for Big Data Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp328-333, July-2018, Available at : http://www.jetir.org/papers/JETIR1807650.pdf