Big Data Density Analytics Using Parallel Coordinate Visualization

Zhang, Jinson; Huang, Mao Lin; Wang, Wen Bo; Lu, Liang; Meng, Zhaopeng

doi:10.1109/cse.2014.219

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…Zhang et al [82] proposed a 5ws parallel coordinate visualization model for big data density analytics. The 5ws is composed of: where did the data generate, when did the data generate, why did the data generate, what was the data content, how was the data transferred and who received the data.…”

Section: Data Visualizationmentioning

confidence: 99%

Energy big data: A survey

et al. 2016

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Energy is one of the most important parts in human life. As a significant application of energy, smart grid is a complicated interconnected power grid that involves sensors, deployment strategies, smart meters, and real-time data processing. It continuously generates data with large volume, high velocity, and diverse variety. In this paper, we first give a brief introduction on big data, smart grid, and big data application in the smart grid scenario. Then, recent studies and developments are summarized in the context of integrated architecture and key enabling technologies. Meanwhile, security issues are specifically addressed. Finally, we introduce several typical big data applications and point out future challenges in the energy domain.

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Section: Data Visualizationmentioning

confidence: 99%

Energy big data: A survey

et al. 2016

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“…Parallel coordinate is one of the most popular tool which is used for visualizing and analyzing data [8]. Most of the researchers are using this visualization method to carry out their research analysis.…”

Section: Parallel Coordinatesmentioning

confidence: 99%

Parallel Coordinates Visualization Tool on the Air Pollution Data for Northern Malaysia

Thomas¹,

Lokanathan²,

Jothi³

2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

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The paper describes based on techniques on the air pollution data visualization. This approach involves graph plotting algorithms with parallel coordinates that explore the raw data with interactive filtering that facilitates the insight of the materials that mixed and harm the population in northern Malaysia. By presenting the parallel coordinates method to visualize the parameter space that influence and visually identify the hazardous, moderate, unhealthy gaseous content in the air. The paper discusses the performance of the chosen visualization method and tested with northern region datasets.

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“…In this paper, we have further developed our previous works presented at I-SPAN 2014 (Zhang et al, 2014) by creating five densities and five parallel axes for Big Data analysis and visualisation. Firstly, we classified Big Data attributes into our 5Ws dimensions based on data characteristics and behaviours, and then illustrated these characteristics using the 5Ws parallel axes to indicate different dimensions.…”

Section: Introductionmentioning

confidence: 99%