Shared-Node Ternary Tree for Multi-Category Search Storage
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Solution Overview
Problem
Conventional ternary tree data structures are limited to storing only one category at a time, requiring separate trees for each search category, which is inefficient and resource-intensive.
Innovation Solution
A single ternary tree is generated that stores multiple categories by reusing and sharing nodes across different categories, allowing common data to be represented using shared nodes, and dynamically resizing arrays to accommodate additional terminologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate ternary trees are created for each search category, then each category can be stored independently, but system resource consumption increases and maintenance complexity worsens
Solution Approach 1:
The patent merges multiple category data structures into a single ternary tree by allowing nodes to store arrays that can accommodate multiple categories. Instead of creating separate trees for each category, the system combines them sharing common nodes and paths, reducing overall structural complexity while maintaining category independence through logical separation within nodes.
Solution Approach 2:
Each node in the ternary tree is designed to be universal by storing an array that can hold data for multiple categories simultaneously. This multi-functional node structure allows the same node to serve different category search purposes, eliminating the need for separate specialized trees for each category while preserving category-specific functionality.
2Measurement precision
If multiple separate ternary trees are maintained for different categories, then category-specific search accuracy is preserved, but memory usage and resource consumption increase
Solution Approach 1:
The patent combines multiple category datasets into a single ternary tree structure, merging previously separate memory resources. By sharing common nodes, prefixes, and paths across categories, the system reduces total memory consumption while maintaining the ability to perform accurate category-specific searches through selective array access within nodes.
Solution Approach 2:
The system discards redundant duplicate storage of common data across multiple separate trees and recovers memory by storing it once in a shared node array. When a node is shared across multiple categories, the common data is stored once and referenced by all categories, eliminating redundant memory usage while preserving search accuracy.
3Reliability
If a single ternary tree stores multiple categories with separate nodes, then category independence is maintained, but data redundancy increases and storage efficiency decreases
Solution Approach 1:
The patent merges multiple category representations into a single unified node structure using arrays. Instead of creating separate nodes for each category at the same position, the system combines them into one node with an array that holds all category data, eliminating redundancy while maintaining category independence through logical separation within the array structure.
Solution Approach 2:
Each node becomes universal by containing an array that can represent multiple categories simultaneously. This multi-functional approach allows a single node to serve multiple category purposes, eliminating the need for redundant separate nodes while preserving the ability to access and search each category independently through the array structure.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed herein that generate a ternary tree data structure that includes multiple categories (e.g., terminologies) using dynamic array modifications that facilitate sharing of one or more nodes across categories. A plurality of different categories may be added and stored within a single ternary tree data structure such that each categories may be separately queried using the single ternary data structure.