Real-Time Exploration of Large Spatiotemporal Datasets Based on Order Statistics

In recent years sophisticated data structures based on datacubes have been proposed to perform interactive visual exploration of large datasets. While powerful, these approaches overlook the important fact that aggregations used to produce datacubes do not represent the actual distribution of the da...

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Bibliographic Details
Published inIEEE transactions on visualization and computer graphics Vol. 26; no. 11; pp. 3314 - 3326
Main Authors Pahins, Cicero A. L., Ferreira, Nivan, Comba, Joao L.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Airports
Approximation
Data structures
Data structures for visualization
Data visualization
Datasets
Delays
event detection
Exploration
Mathematical analysis
order statistics
Production methods
quantile sketch
Real-time systems
Spatiotemporal data
Spatiotemporal phenomena
Statistics
visual analytics
Visualization
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Summary:In recent years sophisticated data structures based on datacubes have been proposed to perform interactive visual exploration of large datasets. While powerful, these approaches overlook the important fact that aggregations used to produce datacubes do not represent the actual distribution of the data being analyzed. As a result, these methods might produce biased results as well as hide important features in the data. In this paper, we introduce the Quantile Datacube Structure (QDS) that bridges this gap by supporting interactive visual exploration based on order statistics. To achieve this, QDS makes use of an efficient non-parametric distribution approximation scheme called p-digest and employs a novel datacube indexing scheme that reduces the memory usage of previous datacube methods. This enables interactive slicing and dicing while accurately approximating the distribution of quantitative variables of interest. We present two case studies that illustrate the ability of QDS to not only build order statistics based visualizations interactively but also to perform event detection on very large datasets. Finally, we present extensive experimental results that validate the effectiveness of QDS regarding memory usage and accuracy in the approximation of order statistics for real-world datasets.
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ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2019.2914446