Partial Database Update via Lightweight Join
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Solution Overview
Problem
Existing database update methods are computationally intensive and inefficient, particularly when updating data across multiple devices, as they often require scanning and transmitting entire databases, which is not suitable for real-time or near real-time updates.
Innovation Solution
A partial database update method using a lightweight join operation that identifies and propagates only updated data fields with times exceeding a propagation time, reducing computational intensity by limiting the scope of the update to unpropagated authoritative values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional database update methods are used to ensure complete data synchronization across multiple devices, then data consistency is maintained, but computational intensity and resource consumption increase significantly
Solution Approach 1:
The patent extracts and transmits only the updated data fields (identified by updated timestamps exceeding the propagation time) rather than the entire database. This selective extraction reduces the volume of data transmitted and processed, directly lowering computational intensity while maintaining data consistency through targeted updates.
Solution Approach 2:
The database update process is segmented into identifying specific updated fields using a lightweight join operation on timestamp criteria, separating them from unchanged data. This segmentation allows the system to process only necessary portions of the database, reducing overall computational load while ensuring complete synchronization of updated elements.
2Reliability
If entire databases are scanned and transmitted to ensure complete updates across devices, then data synchronization is achieved, but network traffic and transmission time increase
Solution Approach 1:
The system extracts only the updated data fields that have timestamps exceeding the propagation time, transmitting minimal necessary data across the network. This extraction approach maintains complete synchronization of updated information while dramatically reducing network traffic and transmission time compared to sending entire databases.
Solution Approach 2:
Instead of performing a complete database scan and transmission (excessive action), the system performs a partial action by scanning only for updated fields using timestamp comparison. This partial approach achieves sufficient synchronization for the update scenario while reducing transmission time and network resource consumption.
3Reliability
If full database joins are performed to identify all data fields for update, then complete data coverage is ensured, but processor cycles and computational resources are excessively consumed
Solution Approach 1:
The patent extracts only the updated data fields by applying a lightweight join operation that filters for records where the update time exceeds the propagation time. This selective extraction ensures complete coverage of updated data while minimizing processor cycles by avoiding joins on unchanged data.
Solution Approach 2:
The system changes the approach from traditional full database joins to a parameter-based filter using timestamps. By comparing update timestamps against the propagation time parameter, the system efficiently identifies only updated fields, improving processor efficiency while maintaining complete coverage of data that requires synchronization.
Data Source
AI summary
A machine is configured to access an authoritative database of data fields, where each data field has a corresponding authoritative value and a corresponding update time. The machine accesses a propagation time that was last updated in response to a most recent propagation of updated authoritative values from the authoritative database to a recipient database. The machine performs a database join that joins only any unpropagated authoritative values having update times that exceed the propagation time to corresponding reference checksums of corresponding data fields. The machine generates candidate checksums of only the corresponding data fields whose unpropagated authoritative values have update times that exceed the propagation time. The machine propagates, from the authoritative database to the recipient database, the unpropagated authoritative values, and the machine replaces the reference checksums with the generated candidate checksums.