Within data-driven artificial intelligence (AI) systems for industrial applications, ensuring the reliability of the incoming data streams is an integral part of trustworthy decision-making. An approach to assess data validity is data quality scoring, which assigns a score to each data point or stream based on various quality dimensions. However, certain dimensions exhibit dynamic qualities, which require adaptation on the basis of the system's current conditions. Existing methods often overlook this aspect, making them inefficient in dynamic production environments. In this paper, we introduce the Adaptive Data Quality Scoring Operations Framework, a novel framework developed to address the challenges posed by dynamic quality dimensions in industrial data streams. The framework introduces an innovative approach by integrating a dynamic change detector mechanism that actively monitors and adapts to changes in data quality, ensuring the relevance of quality scores. We evaluate the proposed framework performance in a real-world industrial use case. The experimental results reveal high predictive performance and efficient processing time, highlighting its effectiveness in practical quality-driven AI applications.

翻译：在面向工业应用的数据驱动人工智能（AI）系统中，确保输入数据流的可靠性是实现可信决策的关键环节。评估数据有效性的一种方法是数据质量评分，该方法基于多种质量维度为每个数据点或数据流分配评分。然而，某些维度表现出动态特性，需要根据系统的当前状态进行自适应调整。现有方法常忽视这一方面，导致其在动态生产环境中效率低下。本文提出自适应数据质量评分操作框架，这是一种为应对工业数据流中动态质量维度挑战而开发的新型框架。该框架通过集成动态变化检测机制引入创新方法，该机制主动监测并适应数据质量的变化，从而确保质量评分的时效性。我们在真实工业应用场景中评估了所提框架的性能。实验结果表明其具有较高的预测性能和高效的处理时间，突显了其在实践性质量驱动AI应用中的有效性。