Screenshot-Guided Data Prefetching to Reduce Application Read Time
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Solution Overview
Problem
Existing applications, particularly those with large data packages, often experience long data reading times, leading to user frustration and game stuttering, due to high performance and bandwidth requirements.
Innovation Solution
A data prefetching method that captures application screenshots and user behavior data to predict and download prefetch data from a server, storing it locally for immediate access, reducing reading times and improving fluency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is read from server during application use, then data is obtained, but reading time is long causing user waiting
Solution Approach 1:
The system performs preliminary actions by capturing screenshots and collecting user behavior data before the actual data reading is needed. This historical data is then used to predict future reading patterns and prefetch data in advance, so that when data is actually needed, it is already available locally, eliminating the long waiting time described in the contradiction.
Solution Approach 2:
The patent implements preliminary data preparation by capturing screenshots and behavior data before the application needs to read data. This advance preparation enables the system to predict what data will be needed and prefetch it, directly resolving the contradiction between long reading time and the need for fast data access.
2Speed
If prefetch data is downloaded and stored locally, then reading speed is improved, but storage occupation increases
Solution Approach 1:
The system extracts only the essential information from screenshots and user behavior data to create prediction models. By taking out only the necessary features and patterns rather than storing all raw data, the system achieves fast reading speeds through prediction while minimizing storage occupation. The model processes only critical data points needed for accurate prediction.
Solution Approach 2:
The patent applies extraction by selecting and processing only the most relevant features from screenshots and behavior logs to build prediction models. This selective extraction allows the system to maintain fast data access through local caching while keeping storage requirements low, as only essential prediction data is retained rather than all raw data.
3Measurement precision
If prediction model is trained with multiple features, then prediction accuracy is improved, but processing complexity increases
Solution Approach 1:
The system segments the complex prediction task into separate modules: screenshot capture, behavior data collection, feature extraction, and prediction modeling. By dividing the complex process into manageable segments, the patent achieves high prediction accuracy through multiple features while reducing the complexity of implementing and training the overall model, as each segment can be optimized independently.
Data Source
AI summary
Provided in the embodiments of the present application are a data reading method and apparatus, and an electronic device. The method includes: acquiring user behavior data and multiple page screenshots of a target application prior to a current moment, the page screenshots are obtained by capturing a display page of the target application; determining, based on the multiple page screenshots and the user behavior data, prefetch data to be read by the target application next time; and downloading the prefetch data from a first server and storing the prefetch data in a local storage device, so that when a next read request for the target application hits the prefetch data, the prefetch data is read from the local storage device.