Dynamic Tag Indexing for Unstructured Data Search Relativity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Typical data indexing systems face challenges in search relativity and findability, especially with unstructured datasets lacking definite identification tags, leading to inefficient search results and increased computing and memory usage.
Innovation Solution
A data indexing system that generates dynamic tags based on search log data, calculating custom, importance, and topic-based weightages to improve search relativity and findability by associating dynamic tags with datasets, using preprocessing techniques and machine learning models to determine relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical data indexing systems use frequency-based search term weighting, then search results can be generated, but search relativity and findability deteriorate for unstructured datasets
Solution Approach 1:
The patent transforms the static frequency-based weighting parameter into a dynamic multi-dimensional weighting system. Instead of relying solely on term frequency, the system calculates custom term weightage (C), important term weightage (I), and topic-based weightage (T) parameters that adapt to unstructured datasets. This parameter transformation enables accurate measurement of search relativity while maintaining high search findability by capturing nuanced relationships between search terms and unstructured data content.
Solution Approach 2:
The patent introduces dynamic tags that are generated and updated based on search log data analysis. These dynamic tags evolve over time as the system learns from user search behaviors and interactions with unstructured datasets. The dynamic nature of these tags allows the system to adapt to changing search patterns and data characteristics, thereby improving both search relativity measurement and overall search findability in unstructured environments.
2Measurement precision
If additional searches are resubmitted to improve search results, then search accuracy may improve, but computing efficiency decreases and processing usage increases
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing dynamic tags and their associated weightage parameters (C, I, T) for unstructured datasets during idle periods or data ingestion phases. When search queries are executed, the system directly retrieves and applies these pre-computed tags instead of performing complex analysis in real-time. This preliminary preparation significantly reduces the computational energy required during actual search operations while maintaining high search accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms that analyze search log data to continuously refine and update dynamic tags and their weightage parameters. By monitoring user search behaviors, click-through rates, and result interactions, the system learns from actual usage patterns and adjusts the C, I, and T parameters accordingly. This feedback loop improves search accuracy over time while reducing the need for multiple search resubmissions, thereby conserving computing energy.
Data Source
AI summary
A data indexing system may use dynamic tags to increase findability and relativity of located datasets. The dynamic tags may be generated based on captured search log data. The system may calculate various parameters used to determine the findability and relativity of a given dataset. The system may calculate a dynamic tag score based on the calculated parameters. The system may store the dynamic tags and the associated dynamic tag score with the given dataset.