AGAMI: Scalable Visual Analytics over Multidimensional Data Streams

As worldwide capability to collect, store, and manage information continues to grow, the resulting datasets become increasingly difficult to understand and extract insights from. Interactive data visualizations offers a promising avenue to efficiently navigate and gain insights from highly complex d...

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Vydáno v:	2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) s. 57 - 66
Hlavní autoři:	Lu, Mingxin, Wong, Edmund, Barajas, Daniel, Li, Xiaochen, Ogundipe, Mosopefoluwa, Wilson, Nate, Garg, Pragya, Joshi, Alark, Malensek, Matthew
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.12.2020
Témata:	Agami Aggregates Big Data big data visualization data analysis data structures data visualisation Data visualization Distributed databases future feature values highly complex datasets in-memory data sketches increasing stream velocities interactive data visualizations interactive visualizations learning (artificial intelligence) Machine learning modern data streams multidimensional data streams online machine learning online machine learning models precomputed representations Predictive models promising avenue query processing real-world meteorological dataset resulting datasets scalable visual analytics Streaming visualization summarizations Uncertainty worldwide capability
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