Iris: Amortized, Resource Efficient Visualizations of Voluminous Spatiotemporal Datasets

The growth in observational data volumes over the past decade has occurred alongside a need to make sense of the phenomena that underpin them. Visualization is a key component of the data wrangling process that precedes the analyses that informs these insights. The crux of this study is interactive...

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Vydané v:	2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) s. 47 - 56
Hlavní autori:	Bruhwiler, Kevin, Buddhika, Thilina, Pallickara, Shrideep, Pallickara, Sangmi Lee
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.12.2020
Predmet:	amortized coprocessors data analysis data visualisation Data visualization data wrangling process dynamic feature selection effective apportioning effective query predicate generation interactive visualizations Iris learning (artificial intelligence) multiple datasets neural nets Neural Networks observational data volumes query processing rendering (computer graphics) resource capacity constraints resource efficient visualizations Servers Sketching Algorithms Spatiotemporal Data spatiotemporal phenomena spatiotemporal visualizations Visualization voluminous datasets voluminous spatiotemporal datasets
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