AI Metadata Generation for Faster Data Catalog Analysis
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Solution Overview
Problem
Manually generating useful metadata for data management is time-consuming and impractical.
Innovation Solution
A data analysis device and method that automatically generates metadata by analyzing data using an analytic query, insight generation, and metadata generation processes, leveraging large language models and models like GPT and T5 for query and insight creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If metadata is manually generated for data management, then the quality and usefulness of metadata can be ensured, but it takes a huge amount of time and is impractical
Solution Approach 1:
The system enables self-service by automatically generating metadata through AI models without requiring manual human intervention. The natural language processing system autonomously analyzes data and generates descriptive metadata, tags, and summaries, allowing the system to serve itself rather than relying on manual operations.
Solution Approach 2:
The patent replaces the mechanical manual process of metadata generation with an automated AI-based system. Large language models and natural language processing algorithms substitute human operators, transforming the metadata generation from a manual mechanical task to an automated intelligent process that maintains quality while dramatically reducing time consumption.
2Loss of time
If automated metadata generation is implemented, then time consumption is reduced, but the quality and usefulness of metadata may deteriorate
Solution Approach 1:
The system introduces an intermediary layer of AI models and natural language processing between the raw data and the generated metadata. This intermediary processes the data through multiple stages including semantic analysis, entity recognition, and language generation, ensuring that automated metadata maintains high quality and usefulness while being generated quickly without manual intervention.
3Loss of information
If complex analysis queries are used to generate insights, then the depth of data understanding is improved, but the complexity of the system increases
Solution Approach 1:
The system employs universal AI models with multi-functionality that can handle various types of data analysis queries. The large language models are designed to perform multiple functions including semantic understanding, insight generation, and metadata creation, reducing the need for separate specialized systems for each analysis task while maintaining deep data understanding capabilities.
Data Source
AI summary
The data analysis device 1X mainly includes an analytic query generation means 52X, an insight generation means 53X, and a metadata generation means 54X. The analytic query generation means 52X is configured to generate, from data, an analytic query for analyzing the data. The insight generation means 53X is configured to generate an insight of the data based on the data and the analytic query. The metadata generation means 54X is configured to generate metadata of the data based on the insight to support decision making.