Database Data Profiler for Predictive Field Identification
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Solution Overview
Problem
Conventional data profiling techniques fail to identify which fields in a database system are predictive of specific outcome values, particularly in complex and dynamic environments where field values change over time and are influenced by categorical, ordinal, or continuous variations, leading to inaccurate data analysis and unreliable outcomes in applications like machine learning and large language models.
Innovation Solution
A database system that includes a data profiler to group records by outcome values, determine field population statistics, and identify correlations between fields and outcomes, using a field evaluator to recommend updates for unpopulated fields, with features like semantic classification and resource management to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data profiling techniques are used, then data analysis can be performed, but the techniques fail to identify predictive fields accurately leading to spurious results
Solution Approach 1:
The patent segments the data analysis process into distinct components: data profiling to assess field population statistics, correlation analysis to identify relationships between fields and outcomes, and predictive field identification. This segmentation allows each component to be optimized independently, improving the accuracy of identifying which fields are predictive of specific outcomes while maintaining reliable data analysis outcomes.
Solution Approach 2:
The system implements feedback mechanisms where correlation analysis results feed back into the data profiling process. The field evaluator uses outcome data to refine field population statistics and update predictions about which fields are predictive. This continuous feedback loop improves measurement precision over time while maintaining reliability through iterative refinement.
2Measurement precision
If data profiling is performed on all database fields, then comprehensive analysis is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts and focuses analysis only on the most relevant fields identified through correlation analysis with outcome fields. Instead of profiling all database fields uniformly, the system extracts the subset of fields that show statistical correlation with outcomes, performing detailed profiling only on these extracted fields. This maintains comprehensive analysis of predictive fields while reducing overall computational resource consumption.
Solution Approach 2:
The system applies different levels of analysis quality to different fields based on their relevance. High-priority fields showing strong correlation with outcomes receive thorough profiling and analysis, while fields with weak or no correlation receive minimal or no profiling. This local differentiation of quality maintains measurement precision for critical fields while reducing energy consumption across the entire dataset.
3Reliability
If field values are updated based on predictions, then data completeness improves, but false updates may occur due to inaccurate predictions
Solution Approach 1:
The system performs preliminary correlation analysis and field evaluation before making any updates to database fields. By pre-identifying which fields are predictive of outcomes and establishing their statistical relationships, the system creates a validated foundation for updates. This preliminary action ensures that only fields with demonstrated predictive value are updated, reducing false updates while improving data completeness.
Solution Approach 2:
The system implements feedback loops where update results are monitored and fed back into the prediction model. Field population statistics are continuously updated based on actual outcomes, refining the accuracy of predictions over time. This feedback mechanism improves measurement precision of predictions while maintaining reliable data completeness through iterative validation.
Data Source
AI summary
A database system may be implemented in a cloud computing environment. The database system may include a storage system storing a first data set including a first plurality of database records including a first plurality of database values corresponding to a first plurality of database fields and a second data set including a second plurality of database records including a second plurality of database values corresponding to a second plurality of database fields. The database system may include a data source profiler configured to determine data set profiling information for the first plurality of database fields and the second plurality of data fields. The database system may include a data source unifier configured to determine and execute one or more operations to unify the first and second data sets.