Big Data Visualization:

Tâm, Nguyễn Thành; Song, Insu

doi:10.1007/978-981-10-0557-2_40

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…Data visualisation is one of the most popular segments within the Big Data industry in the last years. As argued in Section 1, the visual inspection of data has evolved from the derivation of technical methods to attain insights on complex datasets, to an artistic discipline aimed at understanding and interpreting data [106]. For these reasons, visualisation is a vast niche of research, especially in the transportation domain where the geo‐location of data imposes that their visualisation should always reflect the geographical position where the information is captured.…”

Section: Literature Surveymentioning

confidence: 99%

Big Data for transportation and mobility: recent advances, trends and challenges

Torre-Bastida

Ser

Laña

et al. 2018

IET Intelligent Transport Systems

117

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Section: Literature Surveymentioning

confidence: 99%

Big Data for transportation and mobility: recent advances, trends and challenges

Torre-Bastida

Ser

Laña

et al. 2018

IET Intelligent Transport Systems

117

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“…Due to many available forms of visualization and technologies, and analysis methods, many definitions have been proposed. Therefore, no uniformly acceptable definition exists [7].…”

Section: A Data Visualizationmentioning

confidence: 99%

“…Furthermore mobile phone peneration rate in the world has given opporunities to provide cost effective finance, education, and health services [17][18][19][20][21]. However, opportunities came with challenges due to the large amount of data, which included "data acquisition bottleneck" and "information analysis complexity" [7,16,22]. Likewise, Leskovec and Faloutsos [23] pointed out that an Online Social Network (OSN) graph could contain millions of nodes and edges, which made it unfeasible to store the whole graph in the computer's memory.…”

Section: B Performance Issue In Visualization For Big Datamentioning

confidence: 99%

Centrality clustering-based sampling for big data visualization

Nguyen

Song

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Information visualization is essential for improving effectiveness and efficiency of data exploration and knowledge discovery. Therefore, visualization has been used in a wide range of fields from biology, medicine, criminal activity analysis to business and education. Information visualization has become more important than ever as the amount of data being generated has increased dramatically in recent years. One of the major difficulties of information visualization is performance, and this is even more critical when visualizing big data. One potential solution to this challenge is data sampling while maintaining fidelity of visual representation. In this paper, we propose two new centrality clustering-based sampling approaches that apply centrality measures on clusters of data points in order to make more informed sampling than random sampling approaches. We evaluate the new methods on graph data sets. The results show that the new methods significantly outperform existing data sampling methods in term of perceived differences and their ability to preserve essential visual information. Moreover computational complexity is comparable or even better than simple random sampling methods.

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