Recent artificial intelligence (AI) technologies show remarkable evolution in
various academic fields and industries. However, in the real world, dynamic
data lead to principal challenges for deploying AI models. An unexpected data
change brings about severe performance degradation in AI models. We identify
two major related research fields, domain shift and concept drift according to
the setting of the data change. Although these two popular research fields aim
to solve distribution shift and non-stationary data stream problems, the
underlying properties remain similar which also encourages similar technical
approaches. In this review, we regroup domain shift and concept drift into a
single research problem, namely the data change problem, with a systematic
overview of state-of-the-art methods in the two research fields. We propose a
three-phase problem categorization scheme to link the key ideas in the two
technical fields. We thus provide a novel scope for researchers to explore
contemporary technical strategies, learn industrial applications, and identify
future directions for addressing data change challenges.

最近人工智能（AI）技术在各种学术领域和行业展现出了显著的发展。然而，在现实世界中，动态数据给 AI 模型的部署带来了主要的挑战。我们通过设置数据变化来识别两个主要的相关研究领域，即领域转移和概念漂移。虽然这两个热门研究领域旨在解决分布转移和非平稳数据流问题，但其基本属性仍然相似，这也鼓励了相似的技术方法。在这篇综述中，我们将领域转移和概念漂移重新组合为一个研究问题，即数据变化问题，并对这两个研究领域中的最新方法进行了系统概述。我们提出了一个三阶段问题分类方案，以将这两个技术领域中的关键思想联系起来。因此，我们为研究人员提供了一个新的范围，探索当代技术策略，了解工业应用，并确定解决数据变化挑战的未来方向。