The significant growth of interconnected Internet‐of‐Things (IoT) devices, the use of social networks, along with the evolution of technology in different domains, lead to a rise in the volume of data generated continuously from multiple systems. Valuable information can be derived from these evolving data streams by applying machine learning. In practice, several critical issues emerge when extracting useful knowledge from these potentially infinite data, mainly because of their evolving nature and high arrival rate which implies an inability to store them entirely. In this work, we provide a comprehensive survey that discusses the research constraints and the current state‐of‐the‐art in this vibrant framework. Moreover, we present an updated overview of the latest contributions proposed in different stream mining tasks, particularly classification, regression, clustering, and frequent patterns.

中文翻译：

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数据流分析：基础，主要任务和工具

互联物联网（IoT）设备的大量增长，社交网络的使用以及不同领域技术的发展，导致从多个系统连续生成的数据量不断增加。通过应用机器学习，可以从这些不断发展的数据流中获取有价值的信息。实际上，从这些潜在的无限数据中提取有用的知识时，会出现几个关键问题，这主要是由于它们的不断发展的性质和高到达率，这意味着无法完全存储它们。在这项工作中，我们提供了一个全面的调查，讨论了这个充满活力的框架中的研究限制和最新技术。此外，我们提供了在不同流挖掘任务中建议的最新贡献的最新概述，特别是分类，回归，聚类和频繁模式。