Blockchain Analytics System with Permissioned Data Segmentation
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Solution Overview
Problem
Conventional data analytics configurations struggle to effectively process blockchain data due to its decentralized and permissioned nature, making it difficult to provide insights across multiple parties while maintaining privacy and security, especially when analyzing business transactions and behaviors.
Innovation Solution
A method to identify and perform customized data analytics on blockchain data by selecting appropriate data stores and applying analytics processes, with explicit permission control, allowing for the generation of coherent and consistent insights, including aggregated metrics and visualization, using a combination of built-in and custom analytics libraries within a blockchain network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data analytics configurations are used on blockchain data, then data processing can be performed, but the decentralized and permissioned nature of blockchain makes it difficult to provide insights across multiple parties while maintaining privacy and security
Solution Approach 1:
The patent segments blockchain data analytics into multiple layers: data collection layer, data processing layer, and application layer. Each layer operates with specific permission controls and data transformation rules, allowing cross-party analytics while maintaining privacy through selective data exposure and transformation at different segmentation levels.
Solution Approach 2:
The patent introduces smart contracts as intermediaries that mediate between data providers and analytics consumers. These smart contracts enforce permission controls, manage data access rights, and enable collaborative analytics across multiple parties without requiring direct access to raw blockchain data, thus maintaining security while enabling versatility.
2Loss of information
If blockchain data is exposed to machine learning and analytics processes, then insights can be generated, but this creates new complexities in terms of data coherence and consistency
Solution Approach 1:
The patent applies preliminary data transformation and validation processes before blockchain data is exposed to analytics systems. Data is pre-processed, validated, and transformed into analytics-ready formats with embedded metadata that preserves coherence and consistency requirements, reducing complexity during the actual analytics processing stage.
Solution Approach 2:
The patent changes data parameters through transformation processes that convert raw blockchain data into analytics-friendly formats while preserving essential coherence properties. This includes data aggregation, anonymization, and format standardization that maintain data consistency without preventing insightful analytics.
3Reliability
If strict privacy and security control is implemented in permissioned distributed data system, then data security is maintained, but it is not easy to create analytics which provides insight for multiple parties
Solution Approach 1:
The patent implements self-service analytics capabilities where users can define and execute their own analytics queries within the permissioned framework. The system automatically handles permission verification, data access control, and result delivery, enabling efficient analytics creation without manual intervention while maintaining strict privacy controls through automated enforcement of access policies.
Data Source
AI summary
A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include identifying one or more analytic processes to process blockchain data, determining a primary type of data analytic to be performed by the one or more analytic processes, selecting a type of data store to use for performing the one or more data analytic processes based on the primary type of data analytic, accessing the blockchain data, applying the one or more analytic processes, and storing results of the applied analytic processes in a database, file or dashboard. The analytic data may be realized in any manner or preference requested.