The principal goal of data science is to derive meaningful information from
data. To do this, data scientists develop a space of analytic possibilities and
from it reach their information goals by using their knowledge of the domain,
the available data, the operations that can be performed on those data, the
algorithms/models that are fed the data, and how all of these facets
interweave. In this work, we take the first steps towards automating a key
aspect of the data science pipeline: data analysis. We present an extensible
taxonomy of data analytic operations that scopes across domains and data, as
well as a method for codifying domain-specific knowledge that links this
analytics taxonomy to actual data. We validate the functionality of our
analytics taxonomy by implementing a system that leverages it, alongside domain
labelings for 8 distinct domains, to automatically generate a space of
answerable questions and associated analytic plans. In this way, we produce
information spaces over data that enable complex analyses and search over this
data and pave the way for fully automated data analysis.

数据科学的主要目标是从数据中获取有意义的信息，本文尝试自动化数据分析的关键性环节，并通过实现数据分析方法和建立领域特定知识的链接，以及验证功能，为全自动数据分析铺平道路。

自动化数据分析的轻量级知识表示

Lightweight Knowledge Representations for Automating Data Analysis

The United States spends more than $1B each year on initiatives such as the
American Community Survey (ACS), a labor-intensive door-to-door study that
measures statistics relating to race, gender, education, occupation,
unemployment, and other demographic factors. Although a comprehensive source of
data, the lag between demographic changes and their appearance in the ACS can
exceed half a decade. As digital imagery becomes ubiquitous and machine vision
techniques improve, automated data analysis may provide a cheaper and faster
alternative. Here, we present a method that determines socioeconomic trends
from 50 million images of street scenes, gathered in 200 American cities by
Google Street View cars. Using deep learning-based computer vision techniques,
we determined the make, model, and year of all motor vehicles encountered in
particular neighborhoods. Data from this census of motor vehicles, which
enumerated 22M automobiles in total (8% of all automobiles in the US), was used
to accurately estimate income, race, education, and voting patterns, with
single-precinct resolution. (The average US precinct contains approximately
1000 people.) The resulting associations are surprisingly simple and powerful.
For instance, if the number of sedans encountered during a 15-minute drive
through a city is higher than the number of pickup trucks, the city is likely
to vote for a Democrat during the next Presidential election (88% chance);
otherwise, it is likely to vote Republican (82%). Our results suggest that
automated systems for monitoring demographic trends may effectively complement
labor-intensive approaches, with the potential to detect trends with fine
spatial resolution, in close to real time.

本文提出了一种使用深度学习和机器视觉技术从 Google Street View 车辆在美国 200 个城市的街道场景中获取汽车数量信息，进而估计美国社会经济趋势和社会人口统计的方法，结果表明自动化系统可以有效补充劳动密集型方法，并具有实时感。