文章基本信息

标题：Big Data Flow Adjustment Using Knapsack Problem
本地全文：下载
作者：Eyman Yosef ; Ahmed Salama ; M. Elsayed Wahed 等
期刊名称：Journal of Computer and Communications
印刷版ISSN：2327-5219
电子版ISSN：2327-5227
出版年度：2018
卷号：6
期号：10
页码：30-39
DOI：10.4236/jcc.2018.610003
语种：English
出版社：Scientific Research Publishing
摘要：The advancements of mobile devices, public networks and the Internet of creature huge amounts of complex data, both construct & unstructured are being captured in trust to allow organizations to produce better business decisions as data is now pivotal for an organizations success. These enormous amounts of data are referred to as Big Data, which enables a competitive advantage over rivals when processed and analyzed appropriately. However Big Data Analytics has a few concerns including Management of Data, Privacy & Security, getting optimal path for transport data, and Data Representation. However, the structure of network does not completely match transportation demand, i.e., there still exist a few bottlenecks in the network. This paper presents a new approach to get the optimal path of valuable data movement through a given network based on the knapsack problem. This paper will give value for each piece of data, it depends on the importance of this data (each piece of data defined by two arguments size and value), and the approach tries to find the optimal path from source to destination, a mathematical models are developed to adjust data flows between their shortest paths based on the 0 - 1 knapsack problem. We also take out computational experience using the commercial software Gurobi and a greedy algorithm (GA), respectively. The outcome indicates that the suggest models are active and workable. This paper introduced two different algorithms to study the shortest path problems: the first algorithm studies the shortest path problems when stochastic activates and activities does not depend on weights. The second algorithm studies the shortest path problems depends on weights.
关键词：0 - 1 Knapsack Problem;Big Data;Big Data Analytics;Big Dao Ta Inconsistencies