Indian Journal of Science and Technology

Abstract

Background/Objectives: Big data is a relative concept of institutions. Some organizations may see that the data dealt with is very large while it is not worth anything to another organization. The essential target of this study is to investigate the potential effect of big data challenges and provide solution for research issues to improve energy of networks. Methods/ Statistical Analysis: This is based on the third dimension of large data that measures the volume, diversity, variability and complexity of data (Velocity), and data processing speed and processing performance (Velocity). This article gives a stage to investigate enormous information at various stages and it provides the solution to minimize to clean the chunks of the database before pre-processing. In this study a new method, Fused Linear Programming is introduced. We used 18 of previous researches as a reference. Findings: Studies show that companies using large data have achieved 20% growth. Massive data can also allow businesses to analyse millions of tweets on Twitter, for example, to make a decision about a product, on the opinions and comments of the renegade! It also can be used in the health sector to identify and predict diseases, link characteristics and discover their relationship to diseases and drugs. This can be measured by reference to government decisions that have begun to link their decisions and orientations based on the analysis of their accumulated data. This study introduced Fused Linear Programming method to find the veracity of big data impact in energy efficient big data network in bypass Internet protocol over Wavelength Division Multiplexing core networks. Here we present a processing node, which can be integrated with Internet Service Provider data centres to host Internet Protocol and Wavelength Division Multiplexing nodes. The optimized energy saving us up to 56% when there no backup and 43% in the backup node. Improvements/Applications: A tremendous archive of terabytes of stored information is produced every day from current data frameworks and advanced advances, for example, Internet of Things and distributed computing. Analysis of these huge data requires a great deal of endeavours at different levels to retrieve knowledge for decisionmaking. Therefore, big data analysis keeps vital role in the current research and development. Based on the above facts. This research point to the possibility of measuring social interactions among individuals within educational environments to solve problems and collaborative skills, allowing for analysis that is more direct and review of performances related to standard search tools.

Keywords: Big Data, Linear Program, Network, BN, ICT, SN, SPN