Time and temporal constraints are implicit in most databases. To facilitate data analysis and quality assessment, a database should provide explicit operations to identify the violation of temporal constraints. Against this background, the purpose of this paper is threefold: (1) we identify and provide a formal definition of five common anomalies in temporal databases, (2) we propose two new relational operations that allow, respectively, to label anomalous tuples in and to retrieve the anomalous tuples from a dataset, and (3) we provide three different SQL implementations of these operations for current relational database management systems. The healthcare domain is used to illustrate the usage and utility of the temporal anomalies. Finally, an experimental evaluation on real-world and synthetic data analyses the performance of the different implementations of the anomaly operators.

大多数数据库中都隐含着时间和时间约束。为了促进数据分析和质量评估，数据库应提供显式操作来识别违反时间约束的情况。在此背景下，本文的目的有三个：（1）我们识别并提供时态数据库中五种常见异常的正式定义，（2）我们提出两种新的关系操作，分别允许在 和 中标记异常元组从数据集中检索异常元组，(3) 我们为当前的关系数据库管理系统提供了这些操作的三种不同的 SQL 实现。医疗保健领域用于说明时间异常的用途和效用。最后，对现实世界和合成数据的实验评估分析了异常算子的不同实现的性能。