Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from Data to Speed-up Sorted Table Search Procedures:
  Methodology and Practical Guidelines   Sorted Table Search Procedures are the quintessential query-answering tool,
with widespread usage that now includes also Web Applications, e.g, Search
Engines (Google Chrome) and ad Bidding Systems (AppNexus). Speeding them up, at
very little cost in space, is still a quite significant achievement. Here we
study to what extend Machine Learning Techniques can contribute to obtain such
a speed-up via a systematic experimental comparison of known efficient
implementations of Sorted Table Search procedures, with different Data Layouts,
and their Learned counterparts developed here. We characterize the scenarios in
which those latter can be profitably used with respect to the former,
accounting for both CPU and GPU computing. Our approach contributes also to the
study of Learned Data Structures, a recent proposal to improve the time/space
performance of fundamental Data Structures, e.g., B-trees, Hash Tables, Bloom
Filters. Indeed, we also formalize an Algorithmic Paradigm of Learned
Dichotomic Sorted Table Search procedures that naturally complements the
Learned one proposed here and that characterizes most of the known Sorted Table
Search Procedures as having a "learning phase" that approximates Simple Linear
Regression.

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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: eess