Hierarchical Data Structure Optimization via Selective Node Recalculation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional hierarchical relational databases are inefficient for handling frequently changing data sets, as they require recalculation of bound values for all nodes when updates occur, leading to performance degradation and inefficiencies, especially in large datasets.
Innovation Solution
The implementation of a system that uses nested interval data sets combined with adjacency matrices, where only relevant nodes and subtrees are updated, with fixed and equal numerical width bound values for root nodes, and a gap table to manage and fill gaps, reducing the number of recalculation operations and optimizing data structure efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hierarchical relational databases recalculate bound values for all nodes when updates occur, then data integrity is maintained, but computational time and processing efficiency deteriorate significantly
Solution Approach 1:
The patent segments the hierarchical data structure into discrete nodes with hierarchical levels. When an update occurs, only the affected node and its descendant nodes need recalculation, rather than the entire tree. This segmentation allows selective updating of bound values based on the position and scope of changes.
Solution Approach 2:
The patent establishes preliminary actions by pre-defining the hierarchical structure with parent-child relationships and bound value assignments. When updates occur, the system has pre-established rules for propagating changes only to necessary nodes, avoiding full recalculation. The gap table is also pre-configured to track and manage structural changes efficiently.
2Manufacturing precision
If bound values are recalculated for all nodes in frequently changing datasets, then accurate hierarchical relationships are maintained, but productivity and processing speed deteriorate
Solution Approach 1:
The patent applies local quality by making bound values specific to each node's local context within the hierarchy. Each node's bound values are calculated based on its position relative to parent and sibling nodes, not globally across the entire structure. This allows localized recalculation when updates occur, maintaining hierarchical accuracy without full-system processing.
Solution Approach 2:
The patent introduces dynamics by allowing the system to adapt its recalculation scope based on the type and location of updates. The gap table dynamically tracks structural changes, and the system dynamically determines which nodes require bound value recalculation based on the update propagation rules, optimizing processing speed while maintaining accuracy.
3Stability of the object's composition
If the system updates all nodes and subtrees when a node changes, then data consistency is ensured, but device complexity and operational overhead increase
Solution Approach 1:
The patent implements the nested doll principle by organizing data in hierarchical trees where parent nodes contain references to child nodes, which in turn contain their own children. This nested structure allows updates to propagate naturally through the hierarchy from parent to child nodes, ensuring data consistency through the nested relationships rather than requiring external coordination of all nodes.
Solution Approach 2:
The patent introduces the gap table as an intermediary structure that mediates between data updates and bound value recalculation. The gap table tracks structural changes and serves as an intermediate layer that determines which nodes require updates, reducing the complexity of directly managing consistency across the entire hierarchical structure.
4Quantity of substance
If traditional databases handle large hierarchical datasets with frequent updates, then complete data coverage is maintained, but processing efficiency and computational resources worsen
Solution Approach 1:
The patent applies partial action by updating only the necessary portion of the hierarchical structure when changes occur. Instead of recalculating bound values for all nodes (excessive action), the system calculates bound values only for the affected node and its descendants, maintaining complete data coverage while significantly improving computational efficiency for large datasets with frequent updates.
Data Source
AI summary
The disclosure is related to a system for storing and managing hierarchical relationships in a data set. The data set has a plurality of nodes, including root nodes having trees and subtrees, the tree and subtrees including parent, sibling, and child nodes. Each node in the data set has bound values and the bound values of the root nodes are fixed and of equal width. The system further includes processor on a server constructed and arranged to execute a series of steps for optimizing the data set. The processor constructed and arranged to recalculate bound values for updated nodes, sibling nodes and associated subtrees when nodes are updated.