Computational Advances Of Big Data

1147 Words Sep 27th, 2015 5 Pages
In 2013 the overall created and copied data volume in the world was 4.4 ZB and it is doubling in size every two years and, by 2020 the digital universe – the data we create and copy annually – will reach 44 ZB, or 44 trillion gigabytes [1]. Under the massive increase of global digital data, Big Data term is mainly used to describe large-scale datasets. Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making [2]. Volume of Big Data represents the magnitude of data while variety refers to the heterogeneity of the data. Computational advances create a chance to use various types of structured, semi-structured, and unstructured data. Unlike traditional datasets, big data typically includes masses of unstructured data that need more real-time analysis. The unstructured content accounts for 90% of all digital information [3]. Velocity represents the rate at which data are generated and the speed at which it should be analyzed and acted upon. Besides the three V’s, veracity has also been considered as other dimension of big data. Veracity refers to the unreliability inherent in some sources of data. All these dimensions of big data are dependent on each other and a change in any of the dimensions will likely result in a change of another dimension. These properties make it a challenging problem to realize the potential that comes with Big Data.
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