Adaptive Causal Model Configuration Selection
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Solution Overview
Problem
Current causal model configurations are often manually selected and tuned, which is time-consuming and lacks applicability, as they are not systematically optimized for performance in real-time data processing and decision-making.
Innovation Solution
A method for selecting a target causal model configuration based on similarities between datasets, using performance metrics to automatically determine and update the model configuration, enabling adaptive causal modeling and strategy execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual selection and tuning of causal model configurations is used, then the model can be customized for specific scenarios, but the process is time-consuming and lacks systematic optimization
Solution Approach 1:
The patent pre-processes and stores performance data for multiple candidate causal model configurations across different datasets before actual use. When a new data processing task arises, the system quickly retrieves pre-evaluated configuration-performance mappings based on dataset similarity, avoiding time-consuming manual tuning while maintaining scenario-specific adaptability
Solution Approach 2:
The system creates similarity-based copies of performance evaluations from predetermined datasets and applies them to target datasets. By computing dataset similarities and transferring performance knowledge from comparable predetermined datasets, the system avoids re-evaluating each configuration from scratch, significantly reducing configuration time while preserving adaptability
2Reliability
If multiple candidate causal model configurations are evaluated, then the best configuration can be selected for optimal performance, but the complexity of the selection process increases
Solution Approach 1:
The system pre-evaluates multiple candidate causal model configurations on predetermined datasets and stores the performance results. This preliminary evaluation creates a ready-to-use knowledge base that simplifies the selection process for actual data processing tasks, reducing both computational complexity and selection time while maintaining reliable performance optimization
Solution Approach 2:
The patent introduces dataset similarity computation as an intermediary step between the target dataset and candidate configurations. Instead of directly evaluating all configurations on the target dataset, the system uses similarity metrics to bridge to predetermined datasets, where pre-stored performance data guides configuration selection, thereby reducing complexity while ensuring reliable performance
3Measurement precision
If causal model configurations are manually tuned for each dataset, then optimal performance can be achieved, but the process lacks automation and scalability
Solution Approach 1:
The system implements automated feedback loops where performance metrics from applying causal models to datasets are collected and used to update the performance database. This feedback mechanism enables the system to automatically learn and improve configuration selections over time, achieving both precision in performance optimization and high automation without manual intervention
Solution Approach 2:
The patent automatically copies performance evaluation results from predetermined datasets to similar target datasets through similarity-based transfer. This automated copying process eliminates manual tuning while maintaining precise performance optimization by leveraging pre-evaluated configuration performance on comparable datasets
4Productivity
If performance data is stored and updated based on dataset similarities, then configuration selection becomes faster, but the system requires more data storage and processing infrastructure
Solution Approach 1:
The system performs preliminary evaluation and storage of performance data for candidate configurations on predetermined datasets. By preparing this performance knowledge base in advance, the system enables rapid configuration selection for future tasks without re-evaluating all configurations, significantly improving productivity while the storage requirement is offset by avoiding redundant computations
Data Source
AI summary
Embodiments of the present disclosure relate to method, device and computer-readable storage medium for data processing. A method for data processing comprises: obtaining, based on similarities between characteristics of a first target dataset and characteristics of a predetermined dataset, a respective first performance of a plurality of candidate causal model configurations corresponding to characteristics of the predetermined dataset; selecting, based on the respective first performance, a target causal model configuration from the plurality of candidate causal model configurations; and processing the first target dataset using a causal model which is built based on the target causal model configuration. Embodiments of the present disclosure also provide device and computer-readable storage medium capable of implementing the above method. Besides, the embodiments of the present disclosure can adaptively build a good casual model.