Data Record Segmentation for Blockchain Performance
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Solution Overview
Problem
Existing data management systems in blockchain networks face performance issues with slow CRUD operations, timeouts, and failures when handling large or numerous data records, particularly in freight management systems, due to serial execution and lack of flexibility in handling data from blockchain ledgers.
Innovation Solution
The solution involves splitting complex data records into sub-records and using in-memory storage to enable parallel processing and expedite retrieval operations, allowing only necessary data to be fetched and updated, thereby improving performance and avoiding failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data records are stored and retrieved using traditional database management systems with serial execution, then data integrity and consistency are maintained, but CRUD operations become slow and may timeout when handling large or numerous data records
Solution Approach 1:
The patent segments complex data records into multiple smaller sub-records and stores them in an in-memory database. This segmentation allows parallel processing of CRUD operations on different sub-records simultaneously, dramatically improving operation speed and reducing latency when handling large or numerous data records while maintaining data integrity through proper reconstruction of original records when needed
2Adaptability or versatility
If all data values from blockchain ledgers are fetched and stored in data structures, then complete data availability is achieved, but memory consumption increases and operation flexibility decreases
Solution Approach 1:
The patent extracts only the necessary data values from blockchain ledgers based on query requirements rather than fetching all data. The system selectively retrieves specific data fields needed for CRUD operations, storing them in an in-memory database. This extraction approach reduces memory consumption significantly while maintaining operational flexibility, as the system can adapt to different query types by selecting only the required data subsets
3Reliability
If complex data records with multiple data values are processed as single units, then data completeness is ensured, but processing time increases and timeout failures occur
Solution Approach 1:
The patent segments complex data records into smaller sub-records that can be processed independently and in parallel. Each sub-record contains a subset of the original data values, allowing multiple processing operations to occur simultaneously without timeout failures. The system ensures data completeness by properly reconstructing the original complex record from its sub-records when full data retrieval is needed, thus maintaining both reliability and productivity
Data Source
AI summary
Methods that provide accelerated data operations by splitting data records into sub-records and by using in-memory storage. In these methods, a computing device obtains a complex data record that includes at least one unique identifier, primary information about the complex data record, and a plurality of data values that change over time. The computing device generates a plurality of data sub-records by splitting, based on a set of rules, the complex data record into a plurality of parts in which at least a portion of the primary information is separated from the plurality of data values and added to a respective data sub-record of the plurality of data sub-records, generating a unique binding identifier, and adding the unique binding identifier to each of the plurality of data sub-records to link the plurality of data sub-records to each other. The data sub-records are stored, using an in-memory database, into a blockchain.