Application of graph theory in bigdata environment

Supratim Bhattacharya, Jayanta Poray
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引用次数: 3

Abstract

In data driven age, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only in high volume, but also high in variety, velocity & veracity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Graph theory is another flexible domain where we can able to analyze & predict and take decisions effortlessly, comparatively quicker & atmost accurately. In this paper we have reviewed and proposed certain graphical algorithms based on bigdata and analyse their effort towards decision making.
图论在大数据环境中的应用
在数据驱动的时代,决策者可以获得大量的数据。大数据不仅是指大容量的数据集,而且在种类、速度和准确性方面都很高,这使得传统的工具和技术难以处理。由于这些数据的快速增长,需要研究和提供解决方案,以便从这些数据集中处理和提取价值和知识。此外,决策者需要能够从这些多样化和快速变化的数据中获得有价值的见解,从日常交易到客户互动和社交网络数据。图论是另一个灵活的领域,我们可以毫不费力地分析、预测和做出决定,相对更快、最准确。在本文中,我们回顾并提出了一些基于大数据的图形算法,并分析了它们对决策的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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