Hierarchical Database Spreading with Placeholder Nodes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing database platforms face limitations in data spreading operations, such as requiring manual insertion of child nodes and lacking the ability to incorporate dependency rules, which can lead to data inaccuracies and faults during hierarchical data distribution.
Innovation Solution
The introduction of placeholder nodes that act as programmatically manipulable objects, allowing for simultaneous data distribution from multiple parent nodes and enabling the application of spreading rules to ensure data integrity, along with a fault detection module that generates push-up alerts for errors and automatically identifies and corrects faulty rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual insertion of child nodes is used, then data distribution can be performed, but productivity is reduced and time consumption increases
Solution Approach 1:
The system pre-creates placeholder nodes at child positions before data distribution occurs. These placeholders are prepared in advance with the necessary structural framework, allowing the data distribution process to simply populate existing structures rather than creating them manually during the distribution operation.
Solution Approach 2:
The database system automatically performs the data distribution process by utilizing the pre-created placeholder nodes. The system self-manages the creation and population of child nodes without requiring manual user intervention for each insertion operation, enabling automated batch processing of data distribution.
2Reliability
If spreading operations are performed without dependency rules, then data distribution can be executed, but data integrity and accuracy deteriorate
Solution Approach 1:
Dependency rules and spreading logic are defined and stored in advance before data distribution occurs. The system maintains a rule set that specifies how data should be distributed among placeholder nodes, including any necessary calculations, filters, or transformations. This pre-defined logic ensures data integrity is maintained during automated distribution without requiring complex real-time decision-making.
Solution Approach 2:
The system incorporates fault detection mechanisms that monitor the data distribution process and provide feedback when issues arise. When errors such as division by zero or logical conflicts are detected, the system generates notifications that allow users to review and adjust the spreading rules, ensuring data integrity is maintained while providing a manageable complexity level.
3Productivity
If multiple parent nodes are distributed simultaneously, then productivity increases, but fault detection and error resolution become more complex
Solution Approach 1:
The data distribution process is segmented into discrete, manageable units by creating separate placeholder nodes for each child position. Each placeholder node can be independently monitored and validated during the distribution process. This segmentation allows the system to handle multiple parent nodes simultaneously while maintaining clear boundaries for fault detection and error resolution.
Solution Approach 2:
The system implements comprehensive fault detection that monitors each data distribution operation and generates specific notifications when errors occur. The feedback mechanism provides detailed information about which parent node, which child node, and what specific error occurred, enabling targeted resolution without requiring complex analysis of the entire distribution process.
Data Source
AI summary
Embodiments relate to systems and methods for generating a push-up alert of fault conditions in the distribution of data in a hierarchical database. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes, such as quarters, in a hierarchical data store. Parent nodes can be associated with a set of programmatic placeholder nodes. A spreading tool can insert child nodes representing quarters, months, or other insertion point(s) encoded by a placeholder nodes, dividing quantities appropriately. The spreading tool can access or host logic to ensure that data is spread in an order that takes dependencies, formatting, or other factors into account. The spreading tool can also be configured to detect numerical errors, logical errors, or other faults and attempt to identify conflicting rules or other sources of error. Those rules can be automatically removed and/or removed via user selection.