Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selecting Sub-tables for Data Exploration   We present a framework for creating small, informative sub-tables of large
data tables to facilitate the first step of data science: data exploration.
Given a large data table table T, the goal is to create a sub-table of small,
fixed dimensions, by selecting a subset of T's rows and projecting them over a
subset of T's columns. The question is: which rows and columns should be
selected to yield an informative sub-table?
  We formalize the notion of "informativeness" based on two complementary
metrics: cell coverage, which measures how well the sub-table captures
prominent association rules in T, and diversity. Since computing optimal
sub-tables using these metrics is shown to be infeasible, we give an efficient
algorithm which indirectly accounts for association rules using table
embedding. The resulting framework can be used for visualizing the complete
sub-table, as well as for displaying the results of queries over the sub-table,
enabling the user to quickly understand the results and determine subsequent
queries. Experimental results show that we can efficiently compute high-quality
sub-tables as measured by our metrics, as well as by feedback from
user-studies.

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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: nlin
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The categories of this paper are: solv-int