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：在FinTech系统中广泛使用数据限制,以监测数据一致性和诊断异常数据操作;然而,许多等同数据限制在开发周期内是多余的,使FinTech系统放慢速度,造成不必要的警报;我们提出EqDAC,这是确定数据约束等同的有效决策程序;我们首先提出语义编码的象征性表示,然后提出两个轻量级分析,分别反驳和证明在多元时间内证明能达到的等同性;我们评估EqDAC,在30,801个FinTech系统中的数据限制时,我们评估EqDAC,显示EqDAC在3小时内发现11,538个等同数据限制,还支持有效等同搜索,平均时间成本为1.22秒,使该系统能够在提交时检查新的数据限制。