Heuristic Validation Server for Missing Causal Event Identification
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Solution Overview
Problem
Existing validation systems face challenges in efficiently identifying the root cause of discrepancies between data sets generated by different systems, making it a time-consuming process to determine which system is missing an event that caused the mismatch.
Innovation Solution
A validation system that includes a validation server capable of comparing data sets from different systems, identifying differences, grouping mismatches by magnitude, finding associated actions, and applying these actions to data sets to reconcile discrepancies, thereby determining the cause of mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a validation system compares two resultant data sets to validate events affecting the data set, then the system can identify discrepancies between systems, but the root cause analysis becomes a painstaking task of trying to deduce which system is missing a particular event
Solution Approach 1:
The system performs preliminary actions by collecting and storing audit trail information about events affecting the data sets before validation is needed. This pre-collection of event data allows the system to quickly compare what events occurred versus what is reflected in the current data sets, eliminating the need for painstaking manual deduction of missing events during root cause analysis
Solution Approach 2:
The system introduces an intermediary mechanism - the event collection module that captures audit trail information from multiple systems. This intermediary layer systematically records events affecting the data sets, enabling automated comparison and identification of missing events without requiring manual analysis of system logs and audit trails
2Reliability
If manual root cause analysis is performed to deduce which system is missing an event, then the analysis can identify the cause of mismatches, but the process is painstaking and time-consuming
Solution Approach 1:
The validation system performs self-service by automatically collecting event data from multiple systems through audit trails, comparing the collected events against the current data sets, and identifying missing events without human intervention. This automation maintains reliable mismatch identification while dramatically improving validation throughput by eliminating manual analysis processes
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring events affecting data sets and immediately comparing them against the current state. When discrepancies are detected, the system provides feedback about which specific events are missing from which systems, enabling rapid and reliable identification of mismatch causes without manual intervention
Data Source
AI summary
Validating operations are described that determines a first difference between a first data set and a second data set, determines an action associated with the first difference, applies the action to at least one of the first data set and the second data set, determines a second difference between the first data set and the second dataset after applying the action and presents the information including the action applied to at least one of the first data set and the second data set after determining that the second difference satisfies predetermined criteria.