Execution Probability Prediction for Data Transaction Objects
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Solution Overview
Problem
Computer systems face challenges in efficiently predicting which data transaction objects are likely to execute at a desired execution time, leading to wastage of resources on objects unlikely to execute, and struggle to adapt to rapidly changing environments.
Innovation Solution
A computer system uses predictive machine learning models to analyze data transaction objects, generating output messages indicating their probability of execution, and adapts by retraining models to respond to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer systems process large numbers of data transaction objects, then data processing capacity is utilized, but resource waste increases due to processing objects unlikely to execute
Solution Approach 1:
The system performs preliminary prediction of execution probability for data transaction objects before processing them. By using machine learning models to forecast which objects are likely to execute at the desired execution time, the system can prioritize processing resources toward high-probability objects, avoiding waste on objects unlikely to execute.
2Reliability
If computer systems process data transaction objects with many variables and parameters, then processing comprehensive data, but processing time and complexity increase
Solution Approach 1:
The system extracts and focuses on the most critical features and variables from the data transaction objects. By identifying and processing only the essential parameters that most influence execution probability, the system reduces processing time while maintaining comprehensive analysis of the most important data aspects.
3Adaptability or versatility
If computer systems operate in rapidly changing environments, then system adaptability is required, but response time to changes increases
Solution Approach 1:
The machine learning models are designed to dynamically adapt to changing environmental conditions. The system continuously updates its predictions based on new data and changing patterns, enabling rapid response to environmental changes while maintaining high adaptability to diverse and evolving data transaction scenarios.
Data Source
AI summary
A computer system includes a transceiver that receives over a data communications network different types of input data and multiple data transaction objects from multiple source nodes. A pre-processor processes the different types of input data and the data transaction objects to generate an input data structure. Based on the input data structure, one or more predictive machine learning models is trained and used to predict a probability of execution of each of the data transaction objects at a future execution time. Output data messages are then generated for transmission by the transceiver over the data communications network indicating the probability of execution for at least one of the data transaction objects at the future execution time.