Automatic Business Data Capture via Dynamic Time Windows
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Solution Overview
Problem
Conventional business data processing systems rely on manual detection, leading to data loss and initiation failures due to delays between recorded storage times and actual storage times, resulting in inefficient data capture and processing.
Innovation Solution
A business data processing system with a timing module and data detection module that automatically captures changed data by outputting detection commands at preset times, calculating time windows, and initiating deduplicated data to an external business process system, thereby reducing data loss and initiation failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual detection is used to judge whether there is changed business data, then the system can initiate changed data to the corresponding business program, but data loss occurs and initiation failure requires manual correction
Solution Approach 1:
The system enables self-service by implementing an automatic detection mechanism where the data detection module autonomously identifies changed business data without manual intervention. The timing module automatically triggers detection operations at preset times, and the system self-corrects by using overlapping time windows to ensure no data is missed, eliminating the need for manual detection and correction.
Solution Approach 2:
The system implements feedback by having the data detection module continuously monitor the database for changed data, compare it with recorded data to identify deduplicated data, and automatically initiate successful detections while logging failures for correction. This closed-loop feedback mechanism ensures high reliability by automatically detecting and correcting initiation failures without manual intervention.
2Productivity
If detection is performed at fixed intervals, then the system can operate automatically, but time delays cause data loss between storage time and actual storage
Solution Approach 1:
The system applies preliminary action by setting the detection time window to extend before the recorded storage time. This allows the detection operation to capture data that was stored slightly before the expected time, compensating for timing delays. By proactively expanding the detection window backward, the system ensures no data is missed due to synchronization delays between recording and actual storage.
Solution Approach 2:
The system implements dynamics by making the detection time window flexible rather than fixed. The time window adapts based on the recorded storage time and expected delay characteristics, dynamically adjusting its range to ensure comprehensive data capture. This dynamic approach allows the system to maintain automatic operation while compensating for variable storage delays.
3Reliability
If overlapping time windows are used to prevent data loss, then data capture accuracy improves, but detection operation frequency increases
Solution Approach 1:
The system applies partial action by implementing selective deduplication based on comparison with recorded data. Instead of processing all detected data, the system only initiates deduplicated data that has not been previously processed. This approach maintains data completeness through overlapping windows while avoiding redundant processing of already-handled data, thus balancing reliability with operational efficiency.
4Reliability
If deduplication is performed by comparing detection data with recorded data, then initiation failures are reduced, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential comparison operation needed for deduplication - comparing detection data with recorded data to identify unique changed data. By focusing on this specific extraction of necessary comparison logic and implementing it through structured data fields, the system achieves high initiation success rates without excessive complexity. The comparison mechanism is simplified by maintaining organized records of previously initiated data.
Data Source
AI summary
A business data processing system including a storage device and a processor and a method for automatically capturing data are provided. The storage device is used to store a timing module and a data detection module. The processor is coupled to the storage device and is used to execute the timing module and the data detection module. The timing module outputs a detection command to the data detection module at a preset time, so that the data detection module executes a detection operation. The data detection module calculates a time window according to a previous detection operation, captures detection data from a database in the time window, judges whether the detection data has deduplicated data according to the detection data and recorded data, initiates the deduplicated data to an external business process system, obtains corresponding data initiation information from the external business process system, and updates the recorded data.