Rule-Based Feature Catalogs for Nested Data Model Integration
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Solution Overview
Problem
The integration of predictive modeling with complex, nested data structures in data warehousing systems is hindered by the mismatch between data formats required by software engineers for record-keeping and data scientists for analysis, leading to inefficiencies and high engineering efforts in data extraction and model verification.
Innovation Solution
A system and method for creating a feature catalog using rule-based algorithms to extract and normalize data from nested structures into tabular formats compatible with predictive modeling applications, allowing for efficient data access and model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in nested hierarchical structures for long-term storage and record-keeping, then data fidelity and storage efficiency are improved, but data accessibility for predictive modeling and analysis deteriorates
Solution Approach 1:
The system segments data into two distinct storage formats: nested hierarchical structures for long-term archival storage and tabular structures for active predictive modeling. This segmentation allows each data representation to be optimized for its specific purpose without compromise
Solution Approach 2:
The system introduces an intermediary data transformation layer that converts data between nested hierarchical format and tabular format. This intermediary mechanism enables seamless data exchange while maintaining the advantages of both storage approaches
2Productivity
If data is transformed into tabular formats for predictive modeling, then data accessibility and model training efficiency are improved, but engineering effort and processing time increase
Solution Approach 1:
The system performs preliminary data transformation by pre-processing and staging data in tabular formats before predictive modeling begins. This preliminary action reduces the engineering effort required during model training by having data ready in the appropriate format
Solution Approach 2:
The system creates copies of data in both nested hierarchical format (for storage) and tabular format (for modeling). This copying approach allows predictive modeling to proceed efficiently with tabular copies while the original nested structures remain intact for archival purposes
3Measurement precision
If bespoke solutions are developed for each predictive model to access warehoused data, then model-specific accuracy is improved, but system complexity and development time increase
Solution Approach 1:
The system implements a universal data transformation framework that serves multiple predictive modeling functions through a single standardized interface. This universal approach eliminates the need for bespoke solutions for each model while maintaining the ability to access and transform data appropriately for different modeling needs
Data Source
AI summary
Methods, systems, and computer-storage media are provided for generating and populating a feature catalog for use in predictive modeling applications. The feature catalog may be populated with features extracted from data warehoused in a nested, hierarchical data structure. Extraction of features may result from applying a set of rules-based algorithms to warehoused data. Updated features may be extracted from a queue of data updates by applying the same rules-based algorithms. Predictions of future outcomes may be generated by applying predictive models to features.