Input data of a data mining algorithm must conform to a very specific tabular format. Data scientists arrange data into that format by creating long and complex scripts, where different low-level operations are performed, and which can be a time-consuming and error-prone process. To alleviate this situation, we present Lavoisier, a declarative language for data selection and formatting in a data mining context. Using Lavoisier, script size for data preparation can be reduced by $\sim$40% on average, and by up to 80% in some cases. Additionally, accidental complexity present in state-of-the-art technologies is considerably mitigated.

数据挖掘算法的输入数据必须符合非常特定的表格格式。数据科学家通过创建冗长而复杂的脚本将数据整理成这种格式，在脚本中执行不同的低级操作，这可能是一个耗时且容易出错的过程。为了缓解这种情况，我们介绍了Lavoisier，一种用于在数据挖掘上下文中进行数据选择和格式化的声明性语言。使用Lavoisier，可以通过以下方式减少用于数据准备的脚本大小：$〜$平均40％，在某些情况下高达80％。另外，最新技术中存在的意外复杂性也大大降低了。