Test Data Stream State Event Synthesis for Error Repair
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Solution Overview
Problem
Test data often contains errors due to misplaced or missing state events, leading to corrupt and unusable data, which can result in significant loss of testing results.
Innovation Solution
A method and system that access a stream of test data, determine the conformity of data events with the current test state, buffer additional events if necessary, synthesize and publish missing state events, and update the test state accordingly to ensure data events are correctly contextualized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test data is processed without validation, then processing speed is maintained, but data quality and reliability deteriorate due to errors from misplaced or missing state events
Solution Approach 1:
The system performs preliminary validation by checking each data event against the current test state before processing. This preliminary action identifies conforming events that can be published immediately and non-conforming events that require state event synthesis, thereby maintaining data quality without significantly impacting processing speed.
Solution Approach 2:
A state machine serves as an intermediary between raw test data and processed output. It maintains the current test state and mediates the validation process by determining whether data events conform to the expected state, enabling reliable data processing while preserving processing throughput.
2Productivity
If state events are synthesized for non-conforming data events, then data utilization increases, but system complexity increases due to buffering and synthesis logic
Solution Approach 1:
The system performs self-correction by automatically synthesizing missing state events when non-conforming data events are detected. This self-service approach repairs errant data events without external intervention, increasing data utilization while the complexity is managed through automated rather than manual processes.
Solution Approach 2:
The system changes the state parameters by synthesizing new state events with appropriate state identifiers and transitions. This parameter change approach allows the system to correct data errors by introducing synthesized state events that restore proper state context, thereby increasing usable data while managing complexity through systematic state management.
3Loss of information
If all non-conforming data events are repaired through state event synthesis, then loss of information is reduced, but processing time increases due to buffering and synthesis operations
Solution Approach 1:
The system applies partial action by only synthesizing state events when strictly necessary (when data events do not conform to the current test state). Conforming events are published immediately without synthesis overhead. This selective approach minimizes processing time while still preventing data loss for the critical non-conforming events.
Solution Approach 2:
The system skips the synthesis and buffering process for conforming data events, rushing them directly through to publication. This selective fast-tracking reduces overall processing time while ensuring that only the necessary events undergo the more time-consuming synthesis operations, thereby balancing data integrity with processing efficiency.
Data Source
AI summary
In one embodiment, a method of has the steps of A) accessing a stream of test data comprising 1) a number of state events and 2) a number of data events interspersed with the ones of the state events; B) upon accessing one of the data events, determining if the data event is in conformity with the current test state; C) if not, 1) buffering a number of additional data events; and 2) if the number of additional data events imply that a state event should have been received and a state change should have occurred, prior to accessing the data event, i) synthesizing and publishing the state event that should have been received, in conformity with the implied state change; ii) updating the current test state; and iii) then, publishing the data event.