A Survey on Real Time Big Data Analytical Architecture for Remote Sensing Application

Abstract

Recommender systems are found in many applications and these systems usually provide the user with a list of map reduce based on preference and prediction. By combining existing datasets, hybrid recommendation systems can be developed that considers both the job status and job completion time. We can import the web log dataset of size in Terabytes, a big data analysis device such as Hadoop is used. Hadoop is a software construction for scattered processing of large data sets. Hadoop uses Map Reduce model to perform distributed dispensation over clusters of computers to reduce the time involved in analyzing the web log features. The proposed system is reliable and fault tolerant when balanced to the existing approval systems as it collects the data from the user to predict the interest and analyses the item to find the features. The scheme is also adaptive as it updates the list repeatedly and finds the updated interest of the user. Tentative consequences show that the proposed system is more truthful than the existing recommender systems.

Keywords: Fault Tolerant, Map Reduce

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