Before revision:  In this work, we develop a corpus-driven approach to auto-validate machine-generated data by inferring suitable data-validation "patterns" that accurately describe the underlying data-domain , which minimizes false positives while maximizing data quality issues caught.
After revision:  In this work, we develop a corpus-driven approach to auto-validate machine-generated data by inferring suitable data-validation "patterns" that accurately describe the underlying data domain , which minimizes false positives while maximizing data quality issues caught.