Data Value Calculation for High-Value Data Extraction
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Solution Overview
Problem
Current data processing methods face high calculation costs and inefficiencies due to the use of full data sets, which are often contaminated with noise data, making it difficult to evaluate data value and manage data effectively, especially in applications like online medical platforms where resource allocation and prediction models are critical.
Innovation Solution
A method and device for data processing that calculates the value of each data point based on a utility function associated with service revenue, acquires high-value data, and performs service predictions using these values, thereby reducing operational burdens and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full data are used for training and processing, then the data coverage is comprehensive, but the calculation cost increases and noise data affects the results
Solution Approach 1:
The patent extracts high-value data from the full dataset by calculating data values based on a utility function associated with service revenue. This extraction process separates valuable data points from noise data, allowing the system to process only the most relevant data for training and prediction, thereby reducing calculation costs while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces a data value parameter calculated through a utility function that quantifies the contribution of each data point to service revenue. By changing the selection criterion from using all data to selecting data based on their calculated value threshold, the system optimizes the balance between data comprehensiveness and computational efficiency.
2Measurement precision
If full data are used for training, then the model training is comprehensive, but the calculation cost is relatively high
Solution Approach 1:
The patent extracts high-value data from the full dataset by calculating data values based on a utility function associated with service revenue. This extraction process separates valuable data points from noise data, allowing the system to process only the most relevant data for training and prediction, thereby reducing calculation costs while maintaining prediction accuracy.
Solution Approach 2:
Instead of processing the entire dataset, the patent applies partial action by selecting and processing only the subset of data that meets the high-value threshold. This partial processing approach maintains sufficient training quality while significantly improving processing efficiency.
3Productivity
If no data value evaluation is performed, then the data management is simple, but the data use efficiency is not high
Solution Approach 1:
The patent introduces a data value evaluation mechanism as an intermediary layer between data storage and data processing. This intermediary calculates a value score for each data point based on its contribution to service revenue, enabling systematic identification and selection of high-value data without requiring complex manual data management processes.
Data Source
AI summary
A computer-implemented method for data processing based on a data value includes: calculating a value of each piece of original data based on a utility function associated with a service revenue; acquiring pieces of high-value data from the original data based on the value of each piece of original data; and performing a service prediction on the pieces of high-value data.