Hierarchical Database Data Spreading via Placeholder Nodes
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Solution Overview
Problem
Existing database platforms are limited in performing spreading operations across hierarchical data structures, as they restrict data distribution from a single parent node, do not account for dependency rules, and lack the ability to manage multiple sets of spreading rules based on output characteristics.
Innovation Solution
The implementation of placeholder nodes that can be programmatically manipulated to distribute data from parent nodes to child nodes, along with a spreading tool that applies and prioritizes multiple sets of spreading rules to ensure data integrity and accuracy, allowing for multiple passes and iterations of data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is distributed from a single parent node at a time in existing database platforms, then data distribution can be performed with simple control logic, but productivity is reduced due to manual year-by-year insertion and spreading operations
Solution Approach 1:
The patent segments the data distribution process into multiple independent passes, where each pass applies a specific set of spreading rules to a subset of parent nodes. This allows parallel processing of multiple parent nodes while maintaining controlled complexity through rule set management. The segmentation enables automated batch operations instead of manual year-by-year processing.
Solution Approach 2:
The system dynamically selects and applies different sets of spreading rules based on the specific data distribution requirements. Multiple rule sets can be defined with different priorities, and the system automatically determines which rule set to apply in each pass, providing adaptability without requiring complex manual control logic.
2Manufacturing precision
If existing database platforms perform spreading operations without dependency rules, then the operation process is simple, but manufacturing precision deteriorates due to inability to ensure data accuracy and integrity
Solution Approach 1:
The patent implements preliminary action by defining and validating spreading rules before executing data distribution operations. Dependency rules are established in advance to check data integrity conditions (such as verifying charge-offs or carry-over entries) before spreading occurs. This preliminary validation ensures data accuracy without adding complexity during the actual spreading execution.
Solution Approach 2:
The system incorporates feedback mechanisms where the output of each spreading pass is captured and evaluated against expected ranges. If outputs fall outside acceptable ranges, the system can adjust rule application or trigger re-evaluation, ensuring data integrity while maintaining automated operation.
3Adaptability or versatility
If multiple sets of spreading rules are used with different output characteristics, then adaptability improves for different data scenarios, but device complexity increases due to need to manage and qualify multiple rule sets
Solution Approach 1:
The patent manages complexity by parameterizing rule sets with metadata that defines their characteristics, priorities, and applicable conditions. Each rule set can be configured with parameters such as output range expectations and data type specifications. The system automatically selects appropriate rule sets based on these parameters rather than requiring manual management of multiple complex rule configurations.
4Manufacturing precision
If the order of spreading operations is not considered in existing platforms, then the operation process is straightforward, but manufacturing precision deteriorates due to inability to account for fiscal dependencies
Solution Approach 1:
The system performs preliminary ordering of spreading operations based on fiscal dependencies before execution. Rules that must be applied in specific sequences (such as handling carry-over entries from previous fiscal years) are automatically ordered based on their dependency relationships. This preliminary ordering ensures fiscal data integrity without requiring complex real-time decision logic during execution.
Data Source
AI summary
Embodiments relate to systems and methods for generating an optimized output range for a data distribution in a hierarchical database. A data store can store data in a hierarchical format, for instance, in a tree. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes, such as nodes representing quarters. A spreading tool can insert child nodes representing quarters, months, or other at insertion point(s) represented or encoded by a set of placeholder nodes, dividing quantities appropriately. In aspects, the spreading tool can access multiple sets of spreading rules which govern the distribution of data from higher level nodes to lower level nodes. In aspects, the spreading tool can conduct multiple passes of data distribution using different sets of spreading rules, capturing the outputs expressed in the child nodes and selecting rule sets which produce a desired deviation, range, or other characteristics.