Data Intelligence Platform Corpus Schema Validation

The patent introduces a corpus as an intermediary layer between raw data sources and data analysis processes. The corpus standardizes data from multiple sources by defining schemas, data types, and validation rules, thereby improving data quality while maintaining management efficiency. The corpus acts as a mediator that transforms heterogeneous data into a unified, quality-assured format.

The patent performs preliminary data processing and validation by mapping data to a corpus schema before analysis. Data completeness, accuracy, and consistency are verified in advance through schema validation and data quality checks, preventing poor-quality data from propagating through the system and improving overall data reliability.

The patent replaces manual, mechanical data discovery processes with automated machine learning models and algorithms. These models automatically assess data quality metrics such as completeness, accuracy, and consistency by comparing data against the corpus schema, eliminating the need for time-consuming manual inspection while maintaining high measurement precision.

The system enables self-service data quality assessment where the corpus and associated validation rules automatically evaluate data quality without human intervention. The data quality metrics are computed and reported automatically, allowing users to obtain precise data quality assessments without investing significant time in manual analysis.

The patent extracts and standardizes essential data characteristics by mapping data to a predefined corpus schema. This extraction process identifies only the relevant data elements and their relationships, filtering out redundant information. By focusing analysis on the extracted, schema-compliant data rather than raw data from all sources, the system improves data understanding while reducing computing resource requirements.

The patent segments data analysis into distinct stages: data extraction, corpus mapping, validation, and analysis. Each stage processes only the necessary data subset, with validation checks filtering out problematic data early. This segmentation prevents unnecessary processing of invalid or irrelevant data, reducing overall computing resource consumption while maintaining comprehensive data understanding.