Dynamic Encoding Interval Optimization for Object Recovery
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Solution Overview
Problem
Existing data transaction processing systems face challenges in accurately and timely publishing snapshots of frequently updated objects to data recipient systems, especially when incremental changes are frequent, leading to potential inaccuracies or delays in object recovery processes.
Innovation Solution
The system dynamically determines encoding intervals for snapshot data based on historical data about financial instruments within an order book data object, optimizing the amount of new information encoded and transmitted to minimize transmission delay while ensuring accuracy and timeliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the data transaction processing system publishes snapshots frequently to ensure timeliness, then the object recovery accuracy is improved, but the processing burden and system complexity increase
Solution Approach 1:
The patent implements dynamic encoding intervals that automatically adjust based on the frequency of incremental changes. When changes are frequent, the system reduces encoding frequency to lower processing burden; when changes are sparse, it increases encoding frequency to maintain recovery accuracy. This dynamic adaptation resolves the contradiction between maintaining high reliability and reducing system complexity.
Solution Approach 2:
The system changes the parameter of encoding interval dynamically based on observed data change patterns. By adjusting this key parameter rather than maintaining a fixed encoding schedule, the system can optimize between processing burden and recovery accuracy, avoiding unnecessary encoding operations while ensuring timeliness when needed.
2Loss of time
If the system encodes and transmits snapshot data frequently, then the timeliness of object recovery is improved, but the processing burden and computing efficiency deteriorate
Solution Approach 1:
The system employs periodic encoding operations with dynamically adjusted periods. Instead of continuous or fixed-interval encoding, the system performs encoding at optimized intervals based on historical data patterns. This periodic approach with adaptive timing reduces unnecessary processing while ensuring timely updates, resolving the conflict between minimizing transmission delay and maintaining computing efficiency.
Solution Approach 2:
The system performs encoding operations selectively rather than continuously - encoding only when necessary based on the frequency of incremental changes. This partial action approach avoids excessive processing during periods of low data change while ensuring adequate coverage during high-change periods, thereby improving overall computing efficiency without sacrificing timeliness.
3Measurement precision
If the snapshot is updated continuously to maintain accuracy, then the object recovery accuracy is improved, but the transmission delay increases due to processing burden
Solution Approach 1:
The system dynamically adjusts the snapshot update frequency based on the actual rate of incremental changes. When changes occur rapidly, the system increases update frequency to maintain accuracy; when changes are slow, it reduces update frequency to minimize transmission delay. This dynamic adaptation resolves the contradiction between maintaining high precision and reducing time loss.
Data Source
AI summary
The disclosed embodiments relate generally to efficient data encoding and transmission. An encoding system determines an encoding interval at which to encode different groups of related data in a data structure. The encoding interval for each group encoded together optimizes the amount of newly received information that is encoded and transmitted in a continuous, repeating loop.