Hierarchical Decision Tree Data Preprocessing
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Solution Overview
Problem
Current computing systems face inefficiencies in processing large data sets, as they lack effective methods to preprocess and filter data into manageable subsets for real-time decision-making, leading to suboptimal processing efficiency and accuracy.
Innovation Solution
The method involves preprocessing large data sets into smaller subsets based on predetermined associations, sorting and filtering them using conditionally weighted variables, and dynamically updating a hierarchical processing decision tree to improve processing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large data sets are processed directly without preprocessing, then complete data analysis is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides large data sets into smaller subsets through preprocessing operations including sorting data into groups based on predetermined associations and creating reduced data sets. This segmentation allows parallel processing and reduces the computational burden on individual processing units while maintaining overall data completeness through systematic group coverage.
Solution Approach 2:
The patent performs preliminary preprocessing actions before main processing, including sorting data into groups, creating reduced data sets, and establishing hierarchical structures. These preliminary actions organize data in advance to enable faster subsequent processing and reduce the complexity of real-time decision-making operations.
2Productivity
If data is preprocessed into smaller subsets, then processing efficiency improves, but data complexity management becomes more challenging
Solution Approach 1:
The patent implements a hierarchical data structure where reduced data sets are nested within group structures, which are themselves nested within the overall data organization framework. This nested organization allows systematic management of complexity at multiple levels, with each level handling appropriate data granularity and enabling progressive refinement from general to specific processing.
Solution Approach 2:
The patent introduces hierarchical dimensions to data organization by creating multiple levels of data grouping and reduction. Instead of managing all data in a single flat structure, the system adds dimensional layers (groups, subgroups, reduced sets) that transform the complexity management problem into a multi-dimensional space where each dimension handles specific aspects of data organization.
3Measurement precision
If hierarchical processing decision trees are updated dynamically, then real-time decision accuracy improves, but processing overhead increases
Solution Approach 1:
The patent implements periodic updates of the hierarchical processing decision tree rather than continuous updates. The system recursively restarts preprocessing and updates the decision tree at defined intervals or triggers, allowing accurate decision-making while avoiding the constant overhead of real-time continuous updates. This periodic approach balances accuracy with processing efficiency.
Data Source
AI summary
The method disclosed herein includes preprocessing large amounts of input data into a smaller input data sets based on first predetermined associations, sorting the smaller input data sets into groups based on second predetermined associations, filtering grouped data based on upper and lower thresholds, updating a hierarchical processing decision tree within a processor of a computing system, processing data based on the updated hierarchical processing decision tree, and recursively restarting the preprocessing of the large amounts of input data to dynamically update the hierarchical processing decision tree within a user device.