Approximate Query Processing using Deep Generative Models

Abstract

Data is generated at an unprecedented rate surpassing our ability to analyze them. The database community has pioneered many novel techniques for Approximate Query Processing (AQP) that could give approximate results in a fraction of time needed for computing exact results. In this work, we explore the usage of deep learning (DL) for answering aggregate queries specifically for interactive applications such as data exploration and visualization. We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model. The model is often compact - few hundred KBs - so that arbitrary AQP queries could be answered on the client side without contacting the database server. Our other contributions include identifying model bias and minimizing it through a rejection sampling based approach and an algorithm to build model ensembles for AQP for improved accuracy. Our extensive experiments show that our proposed approach can provide answers with high accuracy and low latency.

arXiv commentAccepted to ICDE 2020 as "Approximate Query Processing for Data Exploration using Deep Generative Models"

References in corpus (6)

• Tutorial on Variational Autoencoders
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Data Synthesis based on Generative Adversarial Networks
• Deep Reinforcement Learning for Join Order Enumeration
• Learning to Optimize Join Queries With Deep Reinforcement Learning
• Model-based Approximate Query Processing

Cited by in corpus (8)

• DeepDB: Learn from Data, not from Queries!
• Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration
• DeepSPACE: Approximate Geospatial Query Processing with Deep Learning
• ML-AQP: Query-Driven Approximate Query Processing based on Machine Learning
• NeuroDB: A Neural Network Framework for Answering Range Aggregate Queries and Beyond
• LAQP: Learning-based Approximate Query Processing
• Mosaic: A Sample-Based Database System for Open World Query Processing
• EntropyDB: A Probabilistic Approach to Approximate Query Processing