CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data

CoDDA (Copula-based Distribution Driven Analysis) is a flexible framework for large-scale multivariate datasets. A common strategy to deal with large-scale scientific simulation data is to partition the simulation domain and create statistical data summaries. Instead of storing the high-resolution r...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 25; no. 1; pp. 1214 - 1224
Main Authors: Hazarika, Subhashis, Dutta, Soumya, Han-Wei Shen, Jen-Ping Chen
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Chemical industry
Computational modeling
Computer Science
Computer simulation
Copula
Data models
Data reduction
Data visualization
Datasets
Distribution-based
Flow simulation
Histograms
In situ processing
Multivariate
Multivariate analysis
Probability distribution
Query-driven
Simulation
Spatial data
Statistical analysis
Storage
Summaries
Task analysis
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary:CoDDA (Copula-based Distribution Driven Analysis) is a flexible framework for large-scale multivariate datasets. A common strategy to deal with large-scale scientific simulation data is to partition the simulation domain and create statistical data summaries. Instead of storing the high-resolution raw data from the simulation, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck. Such summaries, often represented in the form of statistical probability distributions, can serve various post-hoc analysis and visualization tasks. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. They are either storage inefficient or are computationally expensive to be estimated in simulation time (in situ) for large number of variables. In this work, using copula functions, we propose a flexible multivariate distribution-based data modeling and analysis framework that offers significant data reduction and can be used in an in situ environment. The framework also facilitates in storing the associated spatial information along with the multivariate distributions in an efficient representation. Using the proposed multivariate data summaries, we perform various multivariate post-hoc analyses like query-driven visualization and sampling-based visualization. We evaluate our proposed method on multiple real-world multivariate scientific datasets. To demonstrate the efficacy of our framework in an in situ environment, we apply it on a large-scale flow simulation.
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SC0007444
USDOE Office of Science (SC)
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2018.2864801