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https://doi.org/10.1109/sera.2...
Article . 2017 . Peer-reviewed
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Article . 2018
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Visualization of online datasets

Authors: Christopher Johnston Downie; Taoxin Peng;

Visualization of online datasets

Abstract

As computing technology advances, computers are being used to orchestrate and advance wide spectrums of commercial and personal life, information visualization becomes even more significant as we immerse ourselves into the era of big data, leading to an economy heavily reliant on data mining and precise, meaningful visualizations. However, accuracy of information visualization techniques is heavily dependent on the knowledge and capabilities of users, leaving novices in many fields at a disadvantage. This is a challenging problem that has been inadequately addressed regardless of the influx in visualization tools. Therefore, this paper proposes a novel approach with a focus on online datasets, allowing users to automatically and accurately visualize datasets. Experiment results show that using a browser extension and specially created HTML tables containing custom attributes - stating the data attribute type - the approach is able to detect and present the most suitable visualizations at the click of a mouse. This proposed approach provides a means for novices to quickly and accurately visualize online datasets.

Keywords

HTML, Browser Extensions, QA75 Electronic computers. Computer science, QA75.5-76.95, Centre for Algorithms, Visualisation and Evolving Systems, Online datasets, Information visualisation, Data transformation, AI and Technologies, 006.6 Computer graphics, Electronic computers. Computer science, Online datasets, Visualization, Browser Extensions, Data transformation, HTML, Software systems, Visualization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
gold