This paper shows the added value of using the existing specific domain knowledge to generate new derivated variables to complement a target dataset and the benefits of including these new variables into further data analysis methods. The main contribution of the paper is to propose a methodology to generate these new variables as a part of preprocessing, under a double approach: creating 2nd generation knowledge-driven variables, catching the experts criteria used for reasoning on the field or 3rd generation data-driven indicators, these created by clustering original variables. And Data Mining and Artificial Intelligence techniques like Clustering or Traffic light Panels help to obtain successful results. Some results of the project INSESS-COVID19 are presented, basic descriptive analysis gives simple results that even though they are useful to support basic policy-making, especially in health, a much richer global perspective is acquired after including derivated variables. When 2nd generation variables are available and can be introduced in the method for creating 3rd generation data, added value is obtained from both basic analysis and building new data-driven indicators.

本文展示了使用现有特定领域知识生成新的派生变量来补充目标数据集的附加值，以及将这些新变量纳入进一步数据分析方法的好处。该论文的主要贡献是提出了一种方法来生成这些新变量作为预处理的一部分，采用双重方法：创建第二代知识驱动变量，捕捉用于现场推理的专家标准或第三代数据-驱动指标，这些指标是通过聚类原始变量创建的。数据挖掘和人工智能技术（如聚类或交通灯面板）有助于获得成功的结果。介绍了 INSESS-COVID19 项目的一些结果，基本的描述性分析给出了简单的结果，即使它们有助于支持基本的政策制定，尤其是在卫生方面，但在包含派生变量后，可以获得更丰富的全球视角。当第二代变量可用并且可以在创建第三代数据的方法中引入时，可以从基础分析和构建新的数据驱动指标中获得附加值。