Blockchain Data Analysis via Distributed Domain-Specific Storage
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Solution Overview
Problem
Data analysis across different domains within global organizations is challenging due to disparate data features, process features, and storage practices, leading to inconsistent formats and unreliable analysis results.
Innovation Solution
Storing both locally configured data and its configurations in a distributed storage platform, such as a blockchain, allows for accurate and meaningful local and global analyses without prior knowledge, enabling audits, variance reporting, and efficient analysis applications by defining parameters specific to each domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple domains with disparate features and configurations is analyzed using traditional centralized methods, then analysis can be performed, but the complexity of data integration and the risk of centralized management increase
Solution Approach 1:
The patent segments the centralized data management system into distributed domain-specific data lakes, where each domain maintains its own data storage and configuration independently. This segmentation eliminates the need for complex centralized integration while preserving global analysis capabilities through standardized query interfaces.
Solution Approach 2:
The patent introduces a standardized configuration schema and query interface as an intermediary layer between diverse domain data lakes and analysis applications. This intermediary enables seamless data integration and analysis across domains without requiring complex point-to-point integration logic.
2Reliability
If data is standardized across all domains to enable global analysis, then analysis consistency improves, but the ability to accommodate local domain requirements and configurations decreases
Solution Approach 1:
The patent applies local quality by allowing each domain to maintain its own data configurations, schemas, and semantics locally while adhering to a standardized configuration structure. This enables domains to customize their data representation to local requirements while ensuring global interoperability through the standardized configuration schema that defines common parameters and relationships.
3Ease of operation
If centralized data storage and management is used, then data access is simplified, but security risks and single points of failure increase
Solution Approach 1:
The patent segments centralized data storage into distributed domain-specific data lakes, eliminating single points of failure. Each domain's data is stored independently, so failures in one domain do not affect others. The segmented architecture maintains operational simplicity through standardized access interfaces while improving reliability through distribution.
Solution Approach 2:
The patent implements feedback mechanisms where domain configurations and data schemas are validated against standardized schemas, and analysis results are aggregated with provenance tracking. This feedback ensures data quality and consistency across domains while maintaining the simplicity of standardized access patterns.
Data Source
AI summary
Systems, methods, and computer media are described for storing and analyzing locally configured data. Both locally configured data and the corresponding data configurations are stored in a distributed storage platform (e.g., a blockchain). Using an accompanying data configuration to interpret and analyze stored data, accurate and meaningful local and global analyses can be performed, across data from different domains, without prior knowledge or external definition of the data. Storage in a distributed storage platform ensures that data cannot be modified and that all transactions are recorded. Example analyses that can be conducted on the stored locally configured data include audits, searching for variances, general queries for data having certain parameters or values, etc.