Abstract
The analysis and processing of big data are one of the most important challenges that researchers are working on to find the best approaches to handle it with high performance, low cost and high accuracy. In this paper, a novel approach for big data processing and management was proposed that differed from the existing ones; the proposed method employs not only the memory space to reads and handle big data, it also uses space of memory-mapped extended from memory storage. From a methodological viewpoint, the novelty of this paper is the segmentation stage of big data using memory mapping and broadcasting all segments to a number of processors using a parallel message passing interface. From an application viewpoint, the paper presents a high-performance approach based on a homogenous network which works parallelly to encrypt-decrypt big data using AES algorithm. This approach can be done on Windows Operating System using .NET libraries.
Highlights
The field of big data analytics is fast becoming attractive to several companies such as banks, telecommunication and web-based companies who generate large volumes of data
Let S be the size of a big file, R as the data bit rate = 1 Gbps, P = {p1, p2, p3, ...} as a number of processes that run in parallel, and let’s consider T as the execution time of data processing
The transmission line was divided into the number of message passing processes that will run in parallel
Summary
The field of big data analytics is fast becoming attractive to several companies such as banks, telecommunication and web-based companies (like Google, Twitter, and Facebook) who generate large volumes of data. The implementation of platforms that are fast enough to load, store and execute ad-hoc analytical queries over big data remains a major challenge [1]. The definition of big data is commonly based on the ‘3Vs: Volume (huge amounts of data), Variety (different forms and emerging structures), and Velocity (fast generation, capturing, and consumption). Being that Hadoop can address the issues relating to volume and velocity in a virtually unlimited horizontal scaling, it has been considered a universal solution; the issue of variety can be addressed by storing data in schema-less databases [2]. Basic Hadoop technologies like HDFS, Pig, Hive, and MapReduce are scalable and cost-efficient; they are Dheyab et al J Big Data (2019) 6:112 associated with certain challenges, especially relating to their ability to support fast ad-hoc analysis and to process difficult queries [3]
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