Researchers currently rely on ad hoc datasets to train automated
visualization tools and evaluate the effectiveness of visualization designs.
These exemplars often lack the characteristics of real-world datasets, and
their one-off nature makes it difficult to compare different techniques. In
this paper, we present VizNet: a large-scale corpus of over 31 million datasets
compiled from open data repositories and online visualization galleries. On
average, these datasets comprise 17 records over 3 dimensions and across the
corpus, we find 51% of the dimensions record categorical data, 44%
quantitative, and only 5% temporal. VizNet provides the necessary common
baseline for comparing visualization design techniques, and developing
benchmark models and algorithms for automating visual analysis. To demonstrate
VizNet's utility as a platform for conducting online crowdsourced experiments
at scale, we replicate a prior study assessing the influence of user task and
data distribution on visual encoding effectiveness, and extend it by
considering an additional task: outlier detection. To contend with running such
studies at scale, we demonstrate how a metric of perceptual effectiveness can
be learned from experimental results, and show its predictive power across test
datasets.

本文介绍了 VizNet，它是一个包含了超过 3100 万个数据集的大规模语料库，从开放数据库和在线可视化画廊中编译而来。它提供了一个必要的公共基线，用于比较可视化的设计技术，并为自动化可视化分析开发基准模型和算法。我们展示了如何从实验结果中学习知觉有效性度量，并展示了它在测试数据集上的预测能力。

VizNet: 一个大规模可视化学习和基准库

VizNet: Towards A Large-Scale Visualization Learning and Benchmarking  Repository

A significant amount of search queries originate from some real world
information need or tasks. In order to improve the search experience of the end
users, it is important to have accurate representations of tasks. As a result,
significant amount of research has been devoted to extracting proper
representations of tasks in order to enable search systems to help users
complete their tasks, as well as providing the end user with better query
suggestions, for better recommendations, for satisfaction prediction, and for
improved personalization in terms of tasks. Most existing task extraction
methodologies focus on representing tasks as flat structures. However, tasks
often tend to have multiple subtasks associated with them and a more
naturalistic representation of tasks would be in terms of a hierarchy, where
each task can be composed of multiple (sub)tasks. To this end, we propose an
efficient Bayesian nonparametric model for extracting hierarchies of such tasks
\& subtasks. We evaluate our method based on real world query log data both
through quantitative and crowdsourced experiments and highlight the importance
of considering task/subtask hierarchies.

该研究旨在提出一种基于贝叶斯非参数模型的任务 / 子任务分层提取模型，有着更好的性能，经过大量的实际数据实验验证，考虑任务 / 子任务的层次结构是至关重要的。