RuleScope: Semantic-aware Authoring of Data Validation Rules

Data validation is a crucial step in data analytics workflows that assesses and ensures the reliability of data flowing into analytical processes. One common approach to data validation involves defining validation rules, which provide explicit constraints and conditions that data must satisfy. However, creating accurate and effective validation rules remains challenging for many practitioners. This challenge stems from the need for practitioners to understand both data structures and their domain-specific semantic relationships. Recent studies have proposed automated approaches to generate validation rules by deriving patterns from data properties. However, these approaches generate rules with limited interpretability and lack support for rule verification and modification, making the rules difficult to understand and adapt. To address these limitations in current validation rule authoring approaches, we present RuleScope, an interactive system for authoring data validation rules through semantic-aware rule generation, visualization, and refinement. RuleScope employs an LLM-based workflow to generate interpretable rules by analyzing data semantics and incorporating domain knowledge. To facilitate rule comprehension, we design a matrix-based visualization that helps users understand rules and analyze validation results. Additionally, RuleScope enables users to interactively refine rules. We evaluate the LLM-based workflow through model evaluation on datasets from different domains and assess RuleScope's usability and effectiveness through two case studies and a user study.