Third-Party Data Synchronization Using Validation Rule Mismatches
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Solution Overview
Problem
SaaS applications often experience data-record mismatches due to delayed notification of deletions or errors, leading to unsynchronized data processing and reporting across systems, which can cause further mismatches and operational issues.
Innovation Solution
Implementing rules for data synchronization that include validation functions and actions to ensure data records are synchronized by comparing evaluations rather than the records themselves, using aggregation to reduce network traffic and resending or triggering manual intervention when mismatches are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data records are synchronized by comparing the records themselves between source and target systems, then data accuracy is improved, but network traffic and processing overhead increase significantly
Solution Approach 1:
The patent extracts the essential validation information from complete data records by implementing validation functions that generate evaluation results. Instead of transmitting and comparing entire data records, the system generates validation evaluations (such as checksums, hashes, or aggregated values) that represent the critical aspects of data integrity. This extraction approach maintains data accuracy while dramatically reducing network traffic and processing overhead.
Solution Approach 2:
The patent transforms the comparison parameter from complete data records to validation function evaluations. By changing what is being compared (from raw data to derived validation metrics), the system achieves the same synchronization goal with reduced resource consumption. The validation functions convert complex data records into simplified evaluation results that are easier and more efficient to transmit and compare across distributed systems.
2Reliability
If complete data records are transmitted between systems for synchronization verification, then data integrity is improved, but network resources and processing time are consumed excessively
Solution Approach 1:
The patent extracts only the essential validation metrics from complete data records rather than transmitting the records themselves. Validation functions process the source data locally to generate compact evaluation results (such as cryptographic hashes or summary statistics) that capture the critical integrity information. This extraction maintains data integrity verification while significantly improving processing efficiency by working with smaller, more manageable data structures.
Solution Approach 2:
The patent segments the data synchronization process into two distinct phases: (1) local validation function execution that processes source data to generate evaluation results, and (2) evaluation comparison that verifies synchronization status. This segmentation allows each phase to operate independently and efficiently, improving overall processing productivity while maintaining data integrity through the validation mechanism.
3Stability of the object's composition
If data synchronization is performed frequently across distributed systems, then data consistency is improved, but network traffic and system load increase
Solution Approach 1:
The patent changes the synchronization parameter from complete data records to validation function evaluations. This parameter transformation enables more frequent synchronization checks with minimal network overhead, as the evaluations are compact and quick to transmit. The system can poll for synchronization status more frequently using these lightweight evaluations, maintaining data consistency without the prohibitive network costs of traditional record-by-record comparison.
Solution Approach 2:
The patent creates compact copies of data integrity information through validation function evaluations rather than copying entire data records. These evaluation copies (such as hashes or checksums) serve as sufficient representations for synchronization verification purposes. The copying approach maintains data consistency verification capability while dramatically reducing the network traffic associated with frequent synchronization operations.
4Measurement precision
If validation functions are implemented to evaluate data records, then synchronization accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial action by implementing validation functions that evaluate only the critical aspects of data records necessary for synchronization verification. Rather than performing exhaustive analysis of complete records, the validation functions focus on specific key fields or generate compact representations (such as selective hashing or targeted validation). This partial evaluation approach maintains sufficient synchronization accuracy while reducing computational overhead compared to complete record processing.
Data Source
AI summary
Synchronizing loosely coupled systems where a third-party system receives from a source system a source evaluation of a validation function based on a data record sent to a target system, and the third party receives from the target system a target evaluation of the validation function based on the data record received by the target system. The third-party system compares the source evaluation to the target evaluation and, in response to a mismatch, the third-party system executes a predefined action for synchronizing the data record at the target system with the data record at the source system. The predefined action may comprise the third-party system sending to the source system a request to resend the data record to the target system.