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TACOMA TRAFFIC VISUALIZATION DRIVERS
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Psychology Press, Hoveīoothe RG (2001) Perception of the visual environment. Multimedia Tools and Applications 77(4):4939–4958Īshby FG (2014) Multidimensional models of perception and cognition. Accessed July 4, 2016Īl-Ayyoub M, AlZu’bi S, Jararweh Y, Shehab MA, Gupta BB (2018) Accelerating 3d medical volume segmentation using GPUs. Using our method, the readers will be able to visualize their network traffic data as an alternative method of t-SNE.Ī: a4 paper size. Our plots also demonstrate the capability of handling a large amount of data.
Comparing with the popular t-SNE method, our visualization method is more flexible and scalable for plotting network traffic data which may require to preserve multi-dimensional information and relationship. We obtain high quality images on a real-world network traffic dataset named ‘ISP’. We combine Principal Component Analysis (PCA) and Mutidimensional Scaling (MDS) to effectively reduce dimensionality and use colormap for enhance visual quality for human beings. This work proposes a novel and effective method for visualizing network traffic data with statistical features of high dimensions. Many existing work fail to provide an effective visualization method for big network traffic data. Such information may be difficult for human to perceive its relationships due to its numeric nature such as time, packet size, inter-packet time, and many other statistical features. Effective visualization allows people to gain insights into the data information and discovery of communication patterns of network flows.
Visualization is an important tool for capturing the network activities.
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