Data constraints are widely used in FinTech systems for monitoring data consistency and diagnosing anomalous data manipulations. However, many equivalent data constraints are created redundantly during the development cycle, slowing down the FinTech systems and causing unnecessary alerts. We present EqDAC, an efficient decision procedure to determine the data constraint equivalence. We first propose the symbolic representation for semantic encoding and then introduce two light-weighted analyses to refute and prove the equivalence, respectively, which are proved to achieve in polynomial time. We evaluate EqDAC upon 30,801 data constraints in a FinTech system. It is shown that EqDAC detects 11,538 equivalent data constraints in three hours. It also supports efficient equivalence searching with an average time cost of 1.22 seconds, enabling the system to check new data constraints upon submission.

翻译：数据约束广泛用于金融科技系统中，以监控数据一致性并诊断异常数据操作。然而，在开发周期中，许多等价的数据约束被重复创建，这不仅拖慢了金融科技系统的运行速度，还引发了不必要的告警。我们提出EqDAC，一种高效的决策程序，用于判定数据约束等价性。我们首先提出语义编码的符号表示方法，随后引入两种轻量级分析，分别用于反驳和证明等价性，并证明这两种分析均可在多项式时间内完成。我们基于一个金融科技系统中的30,801条数据约束对EqDAC进行了评估。结果表明，EqDAC在三小时内检测出11,538条等价数据约束。此外，它支持高效的等价性搜索，平均耗时1.22秒，使得系统能够在提交新数据约束时进行实时检查。