Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Engineering for Data Analytics: A Classification of the Issues, and
  Case Studies   Consider the situation where a data analyst wishes to carry out an analysis
on a given dataset. It is widely recognized that most of the analyst's time
will be taken up with \emph{data engineering} tasks such as acquiring,
understanding, cleaning and preparing the data. In this paper we provide a
description and classification of such tasks into high-levels groups, namely
data organization, data quality and feature engineering. We also make available
four datasets and example analyses that exhibit a wide variety of these
problems, to help encourage the development of tools and techniques to help
reduce this burden and push forward research towards the automation or
semi-automation of the data engineering process.

23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex