Visualization of Big Data Using Nanocubes

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Bol The Big data concept begins with the collection of a large quantity of information and its interpretation. Visualization of this data is extremely effective for presenting essential information. Big-data discovery tools gift new analysis opportunities to the graphics and visual image community. Concerns like storage and process; visualization-based knowledge discovery tools concentrate on the front end of massive data-on serving to businesses explore the information more and become aware of it. Therefore, we tend to attempt to visualize the large knowledge by using Hadoop. The Hadoop package library may be a framework that enables the distribution process of huge knowledge sets across clusters of computers. In this, A visualization technique based on the nanocubes concept is shown, which divides the data into smaller chunks and makes the data more understandable. The visualization of the data is done based on intensity, i.e., the more tweets, the denser the cluster and the more effective will be the visualization.Based upon the intensity, the priority is given to the data which will be visualized by the dark color as compared to the less priority one.

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The Big data concept begins with the collection of a large quantity of information and its interpretation. Visualization of this data is extremely effective for presenting essential information. Big-data discovery tools gift new analysis opportunities to the graphics and visual image community. Concerns like storage and process; visualization-based knowledge discovery tools concentrate on the front end of massive data-on serving to businesses explore the information more and become aware of it. Therefore, we tend to attempt to visualize the large knowledge by using Hadoop. The Hadoop package library may be a framework that enables the distribution process of huge knowledge sets across clusters of computers. In this, A visualization technique based on the nanocubes concept is shown, which divides the data into smaller chunks and makes the data more understandable. The visualization of the data is done based on intensity, i.e., the more tweets, the denser the cluster and the more effective will be the visualization.Based upon the intensity, the priority is given to the data which will be visualized by the dark color as compared to the less priority one.

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Pages: 60, Paperback, LAP Lambert Academic Publishing


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Merk LAP LAMBERT Academic Publishing
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  • 9786207650941
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