Density approach: A new model for BigData analysis and visualization

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Journal Article
Concurrency Computation, 2016, 28 (3), pp. 661 - 673
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Copyright © 2014 John Wiley & Sons, Ltd. In this paper, we extended our density model to BigData analysis and visualization. BigData, which contains images, videos, texts, audio files and other forms of data collected from multiple datasets, is difficult to process and visualize using traditional database management and visualization tools. The challenges are in representing multiple datasets and illustrating and visualizing data patterns to meet business, government and organization needs. We have established the 5Ws density model which uses the 5Ws dimensions for BigData analysis and visualization. The 5Ws dimensions are what the data contain, why the data were transferred, where the data came from, when the data occurred, who received the data and how the data were transferred. According to the network log dataset, an example of BigData, each data incident can be classified into these 5Ws dimensions. The network log dataset ISCX2012 is tested throughout our model. This new model not only classifies network attributes and patterns but also establishes density patterns that provide more analytical features for BigData analysis and visualization. The experimental result shows that this new model with clustered visualization can be efficiently used for BigData analysis and network intrusion detection. Concurrency and Computation: Practice and Experience, 2014.
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