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Basics of Multivariate Analysis in Neuroimaging Data
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CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data.

Subhashis Hazarika, Soumya Dutta, Han-Wei Shen

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    Summary
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    Copula-based Distribution Driven Analysis (CoDDA) offers efficient data reduction for large scientific datasets. This framework enables in situ analysis and visualization of multivariate simulation data, reducing storage needs and improving performance.

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    Area of Science:

    • Data Science
    • Scientific Computing
    • Computational Science

    Background:

    • Large-scale scientific simulations generate massive multivariate datasets.
    • Storing raw simulation data is storage-intensive and creates I/O bottlenecks.
    • Existing multivariate distribution methods are often storage-inefficient or computationally expensive for in situ analysis.

    Purpose of the Study:

    • To introduce a flexible framework, Copula-based Distribution Driven Analysis (CoDDA), for modeling and analyzing large-scale multivariate datasets.
    • To enable efficient data reduction and in situ analysis of scientific simulation data.
    • To facilitate post-hoc analysis and visualization tasks using compact statistical summaries.

    Main Methods:

    • Utilizing copula functions to create a flexible multivariate distribution-based data modeling framework.
    • Developing a method for generating compact statistical data summaries from simulation data.
    • Integrating spatial information storage with multivariate distributions for efficient representation.
    • Implementing query-driven and sampling-based visualization techniques.

    Main Results:

    • CoDDA provides significant data reduction for multivariate datasets.
    • The framework supports efficient in situ analysis and visualization.
    • Spatial information is stored efficiently alongside multivariate distributions.
    • Demonstrated efficacy on real-world scientific datasets and a large-scale flow simulation.

    Conclusions:

    • CoDDA offers a flexible and efficient solution for managing and analyzing large-scale multivariate scientific data.
    • The framework effectively reduces storage overhead and alleviates I/O bottlenecks.
    • CoDDA enables advanced post-hoc analyses and visualizations in an in situ environment.