Automated Metadata Generation via Sample Comparison
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Solution Overview
Problem
Conventional metadata generation methods rely on human judgment and are inefficient when dealing with large datasets, often resulting in errors due to the inability to review all data comprehensively.
Innovation Solution
An automated metadata generating system and method that uses a processor and storage device with data acquisition and analysis modules to perform sample comparison, analyzing original data and generating metadata based on pattern recognition and standard references.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human personnel manually review and establish metadata based on data observation, then metadata accuracy can be maintained through expert judgment, but the processing efficiency and scalability deteriorate when dealing with large volumes of data
Solution Approach 1:
The system enables metadata to be generated automatically through algorithmic analysis of data patterns and characteristics, eliminating the need for manual human review. The processor executes modules that autonomously acquire data, analyze patterns, perform sample comparisons, and generate metadata standards, allowing the system to serve itself rather than relying on external human expertise for each metadata generation task.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system. Instead of personnel manually observing data and making judgment calls, the system uses a processor executing data acquisition modules, data analysis modules, and sample comparison algorithms to automatically generate metadata, substituting human cognitive processes with machine-based automated analysis.
2Productivity
If personnel sample only part of the data to formulate metadata standards, then the workload is reduced to manageable levels, but the reliability and completeness of metadata generation deteriorates due to sampling errors
Solution Approach 1:
The system performs analysis on a representative sample of data but uses algorithmic extrapolation and pattern recognition to generalize findings to the entire dataset. The data analysis module analyzes sampled data thoroughly and the sample comparison module uses these results to generate comprehensive metadata standards that apply to all data, not just the sampled portion, achieving complete coverage through partial analysis enhanced by computational generalization.
Solution Approach 2:
The system incorporates iterative refinement where metadata generation results are continuously validated and improved. The processor executes cycles of data acquisition, analysis, and comparison that refine metadata standards based on feedback from the data patterns observed, ensuring high reliability even when working with sampled data rather than complete datasets.
Data Source
AI summary
The disclosure provides a metadata generating system and a metadata generating method. The metadata generating system includes a storage device and a processor. The storage device stores a data acquisition module and a data analysis module. The processor is coupled to the storage device. The processor executes the data acquisition module to perform data acquisition on the original data and obtain the first data. The processor executes the data analysis module to analyze the first data and generate the second data. The data analysis module performs sample comparison on the second data to generate metadata.