SmartDiff高效精准分析大规模数据差异，支持复杂场景下的可解释验证。
Illuminating Patterns of Divergence: DataDios SmartDiff for Large-Scale Data Difference Analysis
- 通过感知模式的映射与类型专用比较器，自动对齐变化的表结构。
- 在多百万行数据上实现超95%准确率与召回率，性能提升30%-50%。
- 适合数据迁移、回归测试及合规审计等需快速定位问题的场景。
数据工程流程需要在文件、数据库和查询输出间进行可靠的数据比对，但现有工具在模式漂移、异构类型和可解释性方面表现不佳。SmartDiff是一个统一系统，结合了模式感知映射、类型特定比较器和并行执行机制，能够对齐动态演化的表结构，对比结构化与半结构化数据（字符串、数字、日期、JSON/XML），并通过标签聚类结果，解释差异发生的原因与方式。在多百万行数据集上，SmartDiff达到超过95%的精度与召回率，运行速度比基线快30%-40%，内存使用减少30%-50%；用户研究表明，根因分析时间从10小时缩短至12分钟。采用基于LLM的标注流水线，通过检索增强与约束解码生成确定性、符合模式的多标签解释，消融实验显示其在标签准确率与诊断耗时上优于仅依赖规则的基线。这些结果表明SmartDiff适用于迁移验证、回归测试、合规审计与持续数据质量监控。
原文摘要 · Abstract (English)
Data engineering workflows require reliable differencing across files, databases, and query outputs, yet existing tools falter under schema drift, heterogeneous types, and limited explainability. SmartDiff is a unified system that combines schema-aware mapping, type-specific comparators, and parallel execution. It aligns evolving schemas, compares structured and semi-structured data (strings, numbers, dates, JSON/XML), and clusters results with labels that explain how and why differences occur. On multi-million-row datasets, SmartDiff achieves over 95 percent precision and recall, runs 30 to 40 percent faster, and uses 30 to 50 percent less memory than baselines; in user studies, it reduces root-cause analysis time from 10 hours to 12 minutes. An LLM-assisted labeling pipeline produces deterministic, schema-valid multilabel explanations using retrieval augmentation and constrained decoding; ablations show further gains in label accuracy and time to diagnosis over rules-only baselines. These results indicate SmartDiff's utility for migration validation, regression testing, compliance auditing, and continuous data quality monitoring. Index Terms: data differencing, schema evolution, data quality, parallel processing, clustering, explainable validation, big data
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