Graph-Based Dataset Valuation for Predicting AI Task Merit
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Evaluating the applicability of datasets for new AI tasks is challenging due to the non-trivial nature of assessing their usefulness based on historical performance.
Innovation Solution
A system that leverages data lineage information to estimate the merit of datasets by converting lineage graphs into characteristics graphs, using Graph Neural Networks (GNNs) to train regressor models, which predict the merit of datasets for future tasks based on shared characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical performance analysis is used to assess dataset usefulness, then reliability of assessment is improved, but complexity of assessment process increases
Solution Approach 1:
The patent introduces a graph neural network model as an intermediary that automatically processes dataset characteristics and historical performance data to generate merit scores. This intermediary system bridges the gap between raw historical data and actionable assessment insights, reducing manual analysis complexity while maintaining reliability through learned patterns from training data.
Solution Approach 2:
The patent replaces manual or mechanical assessment processes with an automated machine learning system. The graph neural network automatically extracts features, processes historical performance metrics, and generates dataset merit predictions, substituting complex manual analysis workflows with an automated computational system that scales more efficiently.
2Adaptability or versatility
If traditional trial and error methods are used to evaluate datasets, then adaptability to new tasks is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the graph neural network model on extensive historical dataset performance data across multiple tasks. This preliminary training enables the model to learn patterns and relationships that can be quickly applied to new datasets and tasks, eliminating the need for time-consuming trial and error evaluation while maintaining adaptability.
Solution Approach 2:
The patent creates a virtual copy of the assessment process through the graph neural network model, which simulates and predicts dataset performance based on learned patterns from historical data. Instead of physically executing trial and error experiments, the system uses the trained model to copy and replicate the assessment function computationally, dramatically reducing time consumption.
3Measurement precision
If comprehensive data analysis is performed to determine dataset merit, then measurement precision is improved, but computational resources required increase
Solution Approach 1:
The patent extracts only the most relevant characteristics and features from comprehensive dataset information to feed into the graph neural network model. By selectively extracting key attributes rather than processing all available data, the system maintains measurement precision while reducing computational resource consumption through focused feature selection and dimensionality reduction.
Data Source
AI summary
Systems and methods are provided for leveraging data lineage information of datasets to estimate the merit (e.g., worth, value, or importance) of these datasets in performing a future task. For example, the dataset may have been historically applied to train an artificial intelligence (AI) model to perform a task (e.g., an artificial intelligence (AI) task like image recognition or object prediction/detection). The learned merit of the dataset in performing the task may be used as input to train a regressor model, and the trained regressor model can be used to predict future merit of the dataset characteristics in performing another task. The predicted future merit of the dataset characteristics can be mapped to the merit of the dataset in performing another task. The future merit may be related to the same dataset or a different dataset, based on the shared characteristics of the datasets.