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Related Experiment Video

Updated: Jun 7, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

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A Neural Database for Answering Aggregate Queries on Incomplete Relational Data.

Sepanta Zeighami¹, Raghav Seshadri¹, Cyrus Shahabi¹

¹University of Southern California, CA 90089.

IEEE Transactions on Knowledge and Data Engineering

|November 18, 2024

Summary

This summary is machine-generated.

NeuroComplete directly estimates answers to aggregate queries on incomplete datasets, avoiding complex synthetic data generation. This novel approach significantly reduces errors for average and count queries compared to existing methods.

Keywords:

Analytical Queries Machine Learning Missing Data Relational Database

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Area of Science:

Computer Science
Data Science
Machine Learning

Background:

Real-world datasets are frequently incomplete due to cost, privacy, or integration issues.
Incomplete data leads to inaccurate answers for aggregate queries.
Existing solutions involve synthetic data generation, which is challenging and prone to bias.

Purpose of the Study:

To propose a paradigm shift in handling incomplete datasets by directly estimating query answers.
To introduce NeuroComplete, a method that circumvents the need for synthetic data generation.
To improve the accuracy of aggregate query answers on incomplete relational data.

Main Methods:

NeuroComplete generates queries with computable answers from the incomplete dataset.
Queries are embedded into a feature space, representing their relationship with relevant data portions.
A neural network is trained using supervised learning with query features and correct answers.

Main Results:

The NeuroComplete model learns to generalize and accurately estimate answers for new queries.
Experimental results show significant error reduction on real-world datasets.
Up to 4x error reduction for AVG queries and 10x for COUNT queries compared to state-of-the-art methods.

Conclusions:

NeuroComplete offers an effective alternative to synthetic data generation for incomplete datasets.
The approach leverages neural networks and query embedding for accurate aggregate query answering.
This method demonstrates superior performance in reducing errors for common aggregate query types.