Approximate Query Processing using Deep Generative Models
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...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
24.03.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1903.10000 |