Balanced Patricia Tree Data Structure Generation Method
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional tree-type data structures using patricia trees experience unstable search speeds due to unbalanced tree shapes caused by character strings with many common front characters, leading to varying search performance.
Innovation Solution
A method for generating a tree-type data structure that classifies data strings based on the summed types of data at specific string positions, creating balanced child nodes and recursively repeating the classification process to achieve a stable search speed independent of character string characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a patricia tree is generated by creating branches for each common part starting from the beginning of character strings, then the data structure can store multiple data strings efficiently, but the tree becomes unbalanced when character strings have many common front characters, causing unstable search speed
Solution Approach 1:
The patent applies preliminary action by pre-calculating the distribution of data types at each string position across all data strings before tree construction. This statistical analysis is performed in advance to identify optimal classification positions, allowing the tree to be built with balanced structure from the beginning rather than correcting imbalance afterward. The search speed stability is thus ensured through proactive structural optimization.
Solution Approach 2:
The patent changes the parameter used for classification from fixed positional prefixes to statistically determined optimal positions based on data type distribution. By varying the classification criterion from simple prefix matching to data-type-aware position selection, the tree structure achieves balance even when character strings share common front characters, thereby stabilizing search performance across different query patterns.
2Ease of manufacture
If data strings are classified solely by common front characters, then the classification process is simple and fast, but the resulting tree structure becomes unbalanced and search performance varies significantly
Solution Approach 1:
The patent introduces an intermediary statistical analysis layer between the raw data strings and the tree classification process. Instead of directly classifying by common prefixes, the system first computes data type distribution statistics at each position, then uses these statistics as an intermediary guide to select optimal classification positions. This intermediary step balances the simplicity of prefix-based classification with the reliability of balanced tree structure.
Solution Approach 2:
The patent performs preliminary statistical analysis of data type distribution at each string position before the actual classification occurs. This pre-computation identifies positions that will lead to balanced child nodes, allowing the classification process to remain straightforward while ensuring reliable search performance. The preliminary action transforms the classification criterion from naive prefix matching to statistically optimized position selection.
Data Source
AI summary
A method for generating a tree-type data structure composed of a plurality of data strings includes the steps of: summing, with respect to a plurality of data strings classified in a parent node, the numbers of data types of data, respectively, at least one given string position in each of the plurality of data strings; and classifying, based on the numbers of the data types respectively summed at the at least one given string position in the summing step, the plurality of data strings into a plurality of child nodes, for the respective data types at a given string position.