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    3. ELPIS — Graph-Based Similarity Search for Scalable Data Science

    ELPIS — Graph-Based Similarity Search for Scalable Data Science

    VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.

    Overview

    ELPIS is a graph-based similarity search system designed for scalable data science, combining graph-based indexing with optimization techniques for large-scale vector search.

    Key Contributions

    • Graph-based similarity search optimized for large-scale data science workloads
    • Scalable indexing with distributed processing capabilities
    • Effective balance between search accuracy and computational cost
    • Published in VLDB 2023

    Publication

    • Venue: VLDB 2023
    • Authors: Azizi et al.
    • Abbreviation: ELPIS

    Features

    • Graph-based index construction with optimization for distributed environments
    • Efficient search over high-dimensional data at scale
    • Designed for practical data science workloads
    Surveys

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    Information

    Websitewww.vldb.org
    PublishedApr 4, 2026

    Categories

    1 Item
    Research Papers & Surveys

    Tags

    3 Items
    #graph-index#distributed#learning-based

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