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  • PhD Thesis Defense - Sepanta Zeighami

    Wed, Feb 07, 2024 @ 01:00 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Committee members: Cyrus Shahabi (chair), Keith Chugg, Vatsal Sharan, Haipeng Luo
    Title: A Function Approximation View of Database Operations for Efficient, Accurate, Privacy-Preserving & Robust Query Answering with Theoretical Guarantees
    Abstract: Machine learning models have been recently used to replace various database components (e.g., index, cardinality estimator) and provide substantial performance enhancements over their non-learned alternatives. Such approaches take a function approximation view of the database operations. They consider the database operation as a function that can be approximated (e.g., an index is a function that maps items to their location in a sorted array) and learn a model to approximate the operation's output. In this thesis, we first develop the Neural Database (NeuroDB) framework which extends this function approximation view by considering the entire database system as a function that can be approximated. We show, utilizing this framework, that training neural networks that take queries as input and are trained to output query answer estimates provide substantial performance benefits in various important database problems including approximate query processing, privacy-preserving query answering, and query answering on incomplete datasets. Moreover, we present the first theoretical study of this function approximation view of database operations, providing the first-ever theoretical analysis of various learned database operations. Our analysis provides theoretical guarantees on the performance of the learned models, showing why and when they perform well. Furthermore, we theoretically study the model size requirements, showing how model size needs to change as the dataset changes to ensure a desired accuracy level. Our results enhance our understanding of learned database operations and provide the much-needed theoretical guarantees on their performance for robust practical deployment.
    Zoom Link: https://usc.zoom.us/j/91683810479?pwd=VXBmblhDdzZCZU1Oc05jRFV2dzI2dz09
    Meeting ID: 916 8381 0479
    Passcode: 250069

    Location: Charles Lee Powell Hall (PHE) - 106

    Audiences: Everyone Is Invited

    Contact: CS Events

    Event Link: https://usc.zoom.us/j/91683810479?pwd=VXBmblhDdzZCZU1Oc05jRFV2dzI2dz09

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