Target Environment Data Seeding with Referential Integrity
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Solution Overview
Problem
Generating representative test data for software applications is resource-intensive and time-consuming, especially when dealing with cloned or synthetic data, and can be laborious and expensive when using subsets of production data due to referential integrity and sensitive data issues.
Innovation Solution
A method and system for generating a target database by accessing a source database with organized parent and dependent tables, allowing users to select and filter records based on attribute values to create a subset of source records, ensuring accurate representation for testing purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloned production data is used for testing, then data representativeness is improved, but cost and time consumption increase
Solution Approach 1:
The patent uses dynamic data cloning techniques where test data is generated by copying and transforming production data templates. Instead of static cloning, the system creates parametric data models that can be rapidly instantiated with different values, maintaining data representativeness while enabling quick regeneration of test datasets without manual intervention.
Solution Approach 2:
The system performs preliminary actions by pre-defining data templates and schemas during development phases. These templates capture the structure and relationships of production data in advance, allowing rapid generation of representative test data when needed without performing full data cloning at the moment of testing.
2Object-affected harmful factors
If synthetic test data is generated, then privacy requirements are met, but resource intensity and time consumption increase
Solution Approach 1:
The patent employs template-based copying where anonymized production data patterns are captured in templates. These templates preserve the structural characteristics and statistical properties of real data while containing no sensitive information. Test data is generated by populating these templates with synthetic values, achieving privacy protection with minimal computational overhead compared to generating entirely synthetic datasets.
3Loss of time
If subset of production data is used, then cost is reduced, but data integrity issues arise due to referential integrity constraints
Solution Approach 1:
The patent segments production data into modular templates representing different data entities and their relationships. Each template is independently defined with clear boundary conditions and validation rules. This segmentation allows selective instantiation of data subsets without violating referential integrity, as each segment maintains its internal consistency and relationship definitions.
Solution Approach 2:
The system uses parameter-driven data generation where templates contain configurable parameters that control data characteristics. By changing parameters rather than manually curating data subsets, the system can efficiently generate representative data samples that automatically satisfy integrity constraints through the template's built-in validation logic.
4Manufacturing precision
If manual data curation is performed to ensure representativeness, then data quality is improved, but labor intensity increases
Solution Approach 1:
The patent replaces manual data curation with automated template copying. Quality data patterns from production environments are captured in templates once, then automatically replicated and populated for testing. This eliminates repetitive manual work while preserving the quality characteristics embedded in the templates through systematic copying rather than ad-hoc creation.
Solution Approach 2:
The system implements self-service data generation where templates automatically validate and adjust generated data against defined criteria. The templates contain built-in logic for ensuring representativeness and quality without requiring manual intervention, allowing the system to serve itself in generating high-quality test data at scale.
Data Source
AI summary
A method, including accessing a source database including records stored in tables organized in a hierarchy defining respective parent and dependent tables of the tables in the source database and containing respective parent and dependent records of the records, each record including one or more attributes having respective values. A selection is received for at least a first table and one or more second tables that are related to the first table by being parent tables or dependent tables of the first table. A filtering request is received that includes, for a given attribute having one or more distinct values, a limiting number of records to select having each of the distinct values. The request is applied to select a subset of the records in the one of the tables. A target database is generated including the subset and the records that are related to the records in the subset.