This paper proposes a methodology for documenting data mining (DM) projects,
Rastro-DM (Trail Data Mining), with a focus not on the model that is generated,
but on the processes behind its construction, in order to leave a trail (Rastro
in Portuguese) of planned actions, training completed, results obtained, and
lessons learned. The proposed practices are complementary to structuring
methodologies of DM, such as CRISP-DM, which establish a methodological and
paradigmatic framework for the DM process. The application of best practices
and their benefits is illustrated in a project called 'Cladop' that was created
for the classification of PDF documents associated with the investigative
process of damages to the Brazilian Federal Public Treasury. Building the
Rastro-DM kit in the context of a project is a small step that can lead to an
institutional leap to be achieved by sharing and using the trail across the
enterprise.

这篇论文提出了一种用于记录数据挖掘（DM）项目的方法学，Rastro-DM（Trail Data Mining），其关注点不是生成的模型，而是其构建背后的过程，以留下计划行动、完成的训练、获得的结果和经验教训的痕迹。这些提出的实践方法是对 DM 结构化方法学（如 CRISP-DM）的补充，其为 DM 过程建立了方法论和范式框架。最佳实践及其好处的应用在一个名为 'Cladop' 的项目中进行了说明，该项目用于分类与对巴西联邦公共财政的损害调查过程有关的 PDF 文档。在项目背景下构建 Rastro-DM 工具包只是一个小步骤，可以通过在企业内共享和使用痕迹来实现机构性的飞跃。

Rastro-DM: 带有轨迹的数据挖掘

Rastro-DM: data mining with a trail

Given recent successes in AI (e.g., AlphaGo's victory against Lee Sedol in
the game of GO), it's become increasingly important to assess: how close are AI
systems to human-level intelligence? This paper describes the Allen AI Science
Challenge---an approach towards that goal which led to a unique Kaggle
Competition, its results, the lessons learned, and our next steps.

本文介绍了 Allen AI 科学挑战赛及其在人工智能系统接近人类智能方面的结果与收获。