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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Toward Localized Topological Data Structures: Querying the Forest for the Tree.

Pavol Klacansky, Attila Gyulassy, Peer-Timo Bremer

    IEEE Transactions on Visualization and Computer Graphics
    |August 13, 2019
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    Summary
    This summary is machine-generated.

    We introduce the merge forest, a novel data structure for topological data analysis. This approach enhances scalability and reduces memory usage, enabling the analysis of larger datasets.

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    Area of Science:

    • Topological Data Analysis
    • Scientific Visualization
    • High-Performance Computing

    Background:

    • Topological data analysis reveals data features but faces scalability challenges due to global computations.
    • Existing methods struggle with large datasets, limiting the scope of analysis.
    • Data locality is crucial for performance on modern architectures, yet topological computations often require global data access.

    Purpose of the Study:

    • To develop a scalable topological data analysis method for large scalar datasets.
    • To overcome the limitations of global merge tree computations.
    • To improve data locality in topological feature extraction.

    Main Methods:

    • Proposed a merge forest representation, a collection of local trees linked by a bridge set.
    • Implemented a system with linear-time preprocessing and fast query capabilities.
    • Utilized domain decomposition to enhance data locality.

    Main Results:

    • Achieved an order of magnitude performance improvement over traditional methods.
    • Demonstrated scalability on shared-memory parallel systems.
    • Reduced memory overhead, enabling analysis of significantly larger datasets.

    Conclusions:

    • The merge forest provides a scalable and efficient alternative to global merge trees.
    • Data structure locality is key to analyzing large-scale topological data.
    • This approach significantly advances the capabilities of topological data analysis for massive datasets.