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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Scalable Distributed Hashing for Approximate Nearest Neighbor Search.

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    |December 7, 2021
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    Scalable Distributed Hashing (SDisH) improves approximate nearest neighbor search in distributed big data. This model enhances accuracy and efficiency by utilizing a global search radius and voting algorithm, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Data Science
    • Machine Learning

    Background:

    • Hashing is crucial for large-scale approximate nearest neighbor search due to efficiency and low storage needs.
    • Centralized hashing methods are insufficient for distributed big data systems, where data resides across multiple nodes.
    • Traditional distributed methods face challenges with scalability and accuracy, often prioritizing distributed solutions over performance gains.

    Purpose of the Study:

    • To propose a Scalable Distributed Hashing (SDisH) model for efficient and accurate approximate nearest neighbor search in distributed big data environments.
    • To enable seamless integration of existing hashing methods into a distributed framework without modification.
    • To enhance search accuracy by introducing a global search radius and a multi-iteration voting algorithm.

    Main Methods:

    • Developed the Scalable Distributed Hashing (SDisH) model, allowing existing hashing techniques to process distributed data.
    • Implemented a global search radius as a shared variable across nodes to optimize search results iteratively.
    • Introduced a voting algorithm based on multiple iteration results to minimize search errors.

    Main Results:

    • The SDisH model demonstrated significant accuracy improvements, achieving up to 44.75% gain over the aggregating method.
    • Compared to state-of-the-art distributed hashing methods, SDisH showed a 10.23% accuracy increase.
    • Theoretical analysis confirmed the model's superiority in communication, computation, and accuracy.

    Conclusions:

    • The SDisH model offers a scalable and accurate solution for approximate nearest neighbor search in distributed big data.
    • It effectively addresses the limitations of centralized and existing distributed hashing approaches.
    • The proposed model provides substantial accuracy gains and improved efficiency for distributed data search problems.