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  11. Neurosketch: Fast And Approximate Evaluation Of Range Aggregate Queries With Neural Networks.

NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks.

Sepanta Zeighami1, Cyrus Shahabi1, Vatsal Sharan1

  • 1University of Southern California, USA.

Proceedings of the ACM on Management of Data
|May 21, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel machine learning (ML) approach for answering range aggregate queries (RAQs) by modeling queries instead of data. The NeuroSketch framework provides faster, more accurate query results with theoretical guarantees.

Area of Science:

  • Computer Science
  • Machine Learning
  • Database Systems

Background:

  • Range aggregate queries (RAQs) are crucial for real-world applications, often requiring fast, approximate answers.
  • Existing machine learning (ML) models for RAQs lack theoretical understanding and do not leverage query-specific information.
  • Current ML approaches focus on modeling data, limiting performance optimization.

Purpose of the Study:

  • To develop a theoretically grounded ML approach for answering RAQs.
  • To investigate modeling queries rather than data for improved RAQ performance.
  • To introduce a practical framework, NeuroSketch, for efficient RAQ answering.

Main Methods:

  • Developed a novel ML approach by training neural networks to learn query answers directly.
Keywords:
Approximate Query ProcessingMachine LearningTheory of Learned Databases

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  • Focused on modeling queries, enabling theoretical analysis and error bounds.
  • Created the NeuroSketch framework, a neural network-based system for RAQs.

    • Main Results:

    • Established a theoretical framework providing distribution and query-dependent error bounds for neural networks in RAQs.
    • NeuroSketch demonstrates significant performance improvements over state-of-the-art methods.
    • Achieved multiple orders of magnitude speedup and enhanced accuracy on diverse datasets.

    Conclusions:

    • Modeling queries offers a promising direction for ML-based RAQ systems.
    • NeuroSketch provides a theoretically sound and practically efficient solution for RAQs.
    • The approach advances the state-of-the-art in approximate query answering.