Environmental data stream mining is an open challenge for Data Science. Common methods used are static because they analyze a static set of data, and provide static data-driven models. Environmental systems are dynamic and generate a continuous data stream. Dynamic methods coping with the temporal nature of data must be provided in Data Science. Our proposal is to model each environmental information unit, timely generated, as a new case/experience in a Case-Based Reasoning (CBR) system. This contribution aims to incrementally build and manage a Dynamic Adaptive Case Library (DACL). In this paper, a stochastic method for the learning of new cases and management of prototypes to create and manage the DACL in an incremental way is introduced. This stochastic method works with two main moments. An evaluation of the method has been carried using a data stream of air quality of the city of Obregon, Sonora. México, with good results. In addition, other datasets have been mined to ensure the generality of the approach.

环境数据流挖掘是数据科学面临的开放挑战。常用的方法是静态的，因为它们分析静态数据集并提供静态数据驱动的模型。环境系统是动态的，会生成连续的数据流。数据科学中必须提供应对数据时间特性的动态方法。我们的建议是在基于案例的推理（CBR）系统中，将及时生成的每个环境信息单元建模为新的案例/经验。该贡献旨在逐步构建和管理动态自适应案例库（DACL）。本文介绍了一种用于学习新案例和管理原型以增量方式创建和管理DACL的随机方法。这种随机方法适用于两个主要时刻。使用索诺拉州奥布雷贡市的空气质量数据流对该方法进行了评估。墨西哥，取得了不错的成绩。此外，还挖掘了其他数据集以确保该方法的通用性。