Composite-Attribute Tree-Node Diagrams for Data Filtering
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Solution Overview
Problem
Conventional data filtering and mining methods require analyzing multiple fields and reviewing entire datasets to select and analyze data of interest, which is inefficient and time-consuming, especially for large and complex datasets.
Innovation Solution
The use of multiple-level, composite-attribute tree-node diagrams that connect different classes of nodes (A, B, K nodes) to build a tree structure, allowing for the selection and analysis of data of interest without reviewing the whole dataset, by prioritizing attribute nodes and connecting them to represent key attributes, messages, and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data filtering and mining methods are used to review the entire dataset, then complete data coverage is achieved, but time consumption and processing efficiency deteriorate
Solution Approach 1:
The patent segments the dataset into multiple partitions and builds separate tree structures for each partition. This allows parallel processing of different data segments, significantly reducing the time required to review the entire dataset while maintaining complete data coverage through aggregation of results from all partitions.
Solution Approach 2:
The patent performs preliminary actions by pre-building tree structures from historical data before actual filtering queries are executed. These pre-computed tree structures enable rapid data retrieval and filtering operations, eliminating the need to process raw data during query execution and thus reducing time consumption.
2Measurement precision
If multiple fields are analyzed to identify key words for filtering, then filtering accuracy is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent transforms the filtering problem from analyzing multiple fields horizontally to building tree structures vertically across data partitions. The tree structure adds a hierarchical dimension that organizes data by frequency and importance, enabling accurate keyword identification through tree traversal rather than complex multi-field analysis, thus reducing system complexity.
Solution Approach 2:
The patent introduces tree structures as an intermediary data structure between raw data and filtering operations. These trees aggregate frequency information from multiple fields without requiring simultaneous analysis of all fields, simplifying the filtering process while maintaining accuracy through the intermediary's organized representation of data relationships.
3Measurement precision
If the entire dataset is reviewed to select preferred fields, then field selection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential frequency information from the dataset during tree construction, separating this key metric from the full dataset. This extraction allows field selection to be based on pre-computed frequency data rather than reviewing entire records, improving field selection accuracy while dramatically reducing processing time and computational resource requirements.
Solution Approach 2:
The patent applies local quality by building separate tree structures for different data partitions, allowing field selection to be optimized locally for each partition's characteristics. This enables accurate field selection tailored to specific data segments without the computational overhead of analyzing the entire dataset globally, thus improving processing speed while maintaining accuracy.
Data Source
AI summary
Systems and methods for data filtering and mining using multiple-level, composite-attribute tree-node diagrams to quickly select and analyze data of interest.