Metadata-Based Data Valuation Using Hierarchical Structures
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Solution Overview
Problem
Current methodologies fail to quantify the real-time value of data effectively, leading to inefficiencies in data valuation due to high computational loads, competition with production activities, and limitations in accessing encrypted content, and lack of portability across different data lakes and vertical markets.
Innovation Solution
A metadata-based approach that generates hierarchical data structures by analyzing application data sets to create metadata nodes, allowing valuation algorithms to calculate data value without direct access to the data, thus reducing computational needs and enabling faster analysis and value calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data valuation methodologies are used to analyze data sets, then comprehensive data value assessment can be achieved, but computational load increases and analysis speed decreases
Solution Approach 1:
The patent segments the data valuation process into two distinct phases: (1) metadata generation phase where structural information is extracted and stored, and (2) valuation calculation phase where algorithms execute against pre-generated metadata. This segmentation allows comprehensive analysis to be performed offline during metadata generation, while real-time valuation queries execute quickly against the pre-processed metadata structure, thus resolving the contradiction between assessment accuracy and analysis speed.
Solution Approach 2:
The patent performs preliminary actions by generating and storing metadata structures (including data lineage, schema information, and hierarchical relationships) before actual valuation calculations are needed. This preliminary metadata generation enables subsequent valuation algorithms to execute rapidly without re-analyzing the raw data, thereby achieving both comprehensive assessment capability and fast real-time performance.
2Measurement precision
If traditional data valuation methodologies are used to access and analyze data content, then accurate valuation can be achieved, but computational resources are consumed and data security is compromised
Solution Approach 1:
The patent extracts only the essential structural and contextual information from raw data to create metadata representations. Instead of analyzing complete data sets, the system extracts metadata nodes containing schema information, data lineage, relationships, and other structural properties. This extraction approach maintains valuation accuracy by preserving critical data characteristics while dramatically reducing computational resource requirements.
Solution Approach 2:
The patent introduces metadata as an intermediary layer between raw data and valuation algorithms. This metadata intermediary contains all necessary information for accurate valuation (structure, relationships, lineage) without requiring direct access to the actual data content. Valuation algorithms execute against this intermediary metadata representation, achieving accurate assessment with minimal computational resource consumption.
3Measurement precision
If traditional data valuation approaches are used, then data value can be calculated, but portability across different data lakes and vertical markets is limited
Solution Approach 1:
The patent creates a universal metadata schema and hierarchical data structure that can represent diverse data types across different data lakes and vertical markets. The metadata node structure includes standardized elements (data lineage, schema information, relationships) that are applicable regardless of the specific data domain or source system. This universal metadata framework enables the same valuation algorithms to be applied portably across different data lakes and market verticals while maintaining calculation accuracy.
Data Source
AI summary
At least one application data set stored in a data repository is obtained. The application data set is analyzed to generate at least one metadata node. The at least one metadata node is combined with at least one other related node to form a hierarchical data structure. One or more valuation algorithms are executed against the hierarchical data structure to calculate a value for the data set represented in the hierarchical data structure.