Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neo: A Learned Query Optimizer   Query optimization is one of the most challenging problems in database
systems. Despite the progress made over the past decades, query optimizers
remain extremely complex components that require a great deal of hand-tuning
for specific workloads and datasets. Motivated by this shortcoming and inspired
by recent advances in applying machine learning to data management challenges,
we introduce Neo (Neural Optimizer), a novel learning-based query optimizer
that relies on deep neural networks to generate query executions plans. Neo
bootstraps its query optimization model from existing optimizers and continues
to learn from incoming queries, building upon its successes and learning from
its failures. Furthermore, Neo naturally adapts to underlying data patterns and
is robust to estimation errors. Experimental results demonstrate that Neo, even
when bootstrapped from a simple optimizer like PostgreSQL, can learn a model
that offers similar performance to state-of-the-art commercial optimizers, and
in some cases even surpass them.

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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: quant-ph