The General Data Protection Regulation (GDPR) came into force in 2018. After this enforcement, many fines have already been imposed by national data protection authorities in Europe. This paper examines the individual GDPR articles referenced in the enforcement decisions, as well as predicts the amount of enforcement fines with available meta-data and text mining features extracted from the enforcement decision documents. According to the results, three articles related to the general principles, lawfulness, and information security have been the most frequently referenced ones. Although the amount of fines imposed vary across the articles referenced, these three particular articles do not stand out. Furthermore, a better statistical evidence is available with other meta-data features, including information about the particular European countries in which the enforcements were made. Accurate predictions are attainable even with simple machine learning techniques for regression analysis. Basic text mining features outperform the meta-data features in this regard. In addition to these results, the paper reflects the GDPR's enforcement against public administration obstacles in the European Union (EU), as well as discusses the use of automatic decision-making systems in judiciary.

翻译：《一般数据保护条例》(GDPR)于2018年生效。执行之后,欧洲国家数据保护当局已处以许多罚款。本文件审查了执行决定中提及的GDPR个别条款,并用从执行决定文件中提取的现有元数据和文字采矿特征预测了强制执行罚款的数额。根据结果,与一般原则、合法性和信息安全有关的三条条款是最经常引用的条款。尽管对所提及的条款所实施的罚款数额不同,但这三个特定条款并不突出。此外,还有其他元数据特征有更好的统计证据,包括关于执行决定的具体欧洲国家的信息。即使采用简单的机械学习方法进行回归分析,也能够实现准确的预测。基本文本采矿特征超过了这方面的元数据特征。除了这些结果外,本文件还反映了GDPR针对欧洲联盟(欧盟)公共行政障碍的执法情况,并讨论了司法中自动决策系统的使用情况。