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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Podrobná bibliografie
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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Popis
Shrnutí:	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 datasets, but the velocity of modern data streams often means that precomputed representations or summarizations of the data will quickly become obsolete. Our system, Agami, provides live-updating, interactive visualizations over streaming data. We leverage in-memory data sketches to summarize and aggregate information to be visualized, and also allow users to query future feature values by leveraging online machine learning models. Our approach facilitates low-latency, iterative exploration of data streams and can scale out incrementally to handle increasing stream velocities and query loads. We provide a thorough evaluation of our data structures and system performance using a real-world meteorological dataset.
DOI:	10.1109/BDCAT50828.2020.00020