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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Bibliographic Details
Published in:2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) pp. 57 - 66
Main Authors: Lu, Mingxin, Wong, Edmund, Barajas, Daniel, Li, Xiaochen, Ogundipe, Mosopefoluwa, Wilson, Nate, Garg, Pragya, Joshi, Alark, Malensek, Matthew
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2020
Subjects:
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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Summary: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