Illuminating Patterns of Divergence: DataDios SmartDiff for Large-Scale Data Difference Analysis

2025-08-29

AI Summary

Addressing challenges in data engineering—including schema drift, difficulty handling heterogeneous data types, and insufficient interpretability in file, database, and query-result diffing—this paper introduces the first unified, scalable differential analysis framework. Our method integrates schema-aware mapping, type-specific comparators, and an LLM-enhanced retrieval-constrained multi-label explanation generator to enable high-accuracy comparison and root-cause localization across structured and semi-structured data. Evaluated on million-row datasets, the framework achieves >95% precision and recall, outperforms baselines by 30–40% in throughput, reduces memory consumption by 30–50%, and shortens root-cause analysis time from 10 hours to 12 minutes. These advances significantly improve reliability and interpretability in data migration validation, regression testing, and regulatory compliance auditing.

Technology Category

Data Mining & Knowledge Management: Scalability, Parallel & Distributed SystemsSearch and Optimization: Distributed SearchKnowledge Representation and Reasoning: Description Logics

Application Category

Web Mining and Content Analysis: Bridging structured and unstructured dataSemantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologiesSearch and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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

Problem

• Handling schema drift and heterogeneous data types
• Providing explainable differences in large-scale data
• Improving efficiency and accuracy in data comparison

Innovation

• Schema-aware mapping with type-specific comparators
• Parallel execution for efficient large-scale analysis
• LLM-assisted labeling with deterministic multilabel explanations