一句话：提出 AutoReconciler，从业务数据挖掘数据约束并自动生成 SQL 对账脚本。
软件异常行为导致的资金损失在金融科技与电商中日益严重，对账是常用检测手段，但其核心数据约束与对账脚本通常依赖业务专家手工构建。本文提出 AutoReconciler，从业务数据中挖掘由前置条件与断言构成的数据约束，并自动生成 SQL 对账脚本：用增强符号回归挖掘断言，用关联规则挖掘发现前置条件。在合成数据上，方法在精确率和召回率上平均优于基线 22.1% 和 51.6%，尤其擅长复杂约束；系统已实现并部署于生产，案例研究验证了工业场景收益。
Fund loss is an increasingly critical problem caused by the misbehavior of software, especially in fintech and e-commerce platforms. Data reconciliation is one of the most commonly used approaches in detecting and preventing fund loss by executing reconciliation scripts on data storage systems (e.g., database and cache systems). The core of reconciliation scripts is the data constraints, which can be expressed as implications with two parts: preconditions and assertions. However, due to the complexity and diversity of business, the construction of data constraints and reconciliation scripts usually heavily relies on business experts. To this end, we propose AutoReconciler to mine data constraints from business data and generate reconciliation scripts automatically. It can mine assertions via enhanced symbolic regression, discover preconditions via association rule mining, and generate reconciliation scripts in SQL form. We have performed extensive experiments on the synthesized data. The result shows that our approach outperforms the baseline by a large margin (an average improvement in precision and recall of 22.1% and 51.6%, respectively), especially for complex data constraints. Our solution has been implemented, deployed, and adopted in production, and we conducted several case studies further to confirm the benefits of our solution in industrial scenarios.
@inproceedings{WangZZWYD23,
title = {Data Constraint Mining for Automatic Reconciliation Scripts Generation},
author = {Tianxiao Wang and Chen Zhi and Xiaoqun Zhou and Jinjie Wu and Jianwei Yin and Shuiguang Deng},
booktitle = {ISSTA},
year = {2023},
doi = {10.1145/3597926.3598122}
}