Preloading Application Resources via Historical Scene Similarity
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Solution Overview
Problem
Mobile terminals experience a significant delay in starting applications due to the time-consuming process of loading startup resources, such as code, user interface materials, and data, when a user clicks on an application icon.
Innovation Solution
A method and apparatus for preloading resources by analyzing current and historical scene data to predict which applications are likely to be started next, based on similarities in user, environment, and device attributes, and preloading the necessary resources into memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If startup resources are loaded only when the user clicks on an application icon, then the device memory usage is optimized, but the application startup time becomes excessively long
Solution Approach 1:
The system performs preliminary actions by preloading startup resources into memory based on historical data analysis and similarity calculation. When a user clicks an application icon, the system determines whether to load resources by comparing current scene attributes with historical scenes, and pre-loads the necessary startup resources (code, UI materials, data) before the application is actually launched, thereby reducing startup time without permanently occupying excessive memory
2Loss of time
If all startup resources are preloaded into memory, then the application startup time is minimized, but the device memory consumption increases significantly
Solution Approach 1:
The system applies local quality by selectively loading only the specific startup resources needed for the predicted application rather than loading all possible resources. Based on the determined historical scene and similarity calculation, the system identifies and loads only the relevant code, UI materials, and data for the specific application that is likely to be launched, optimizing both startup time and memory usage
3Measurement precision
If the system analyzes historical data and calculates similarities to predict applications, then the startup resource loading accuracy is improved, but the system complexity increases
Solution Approach 1:
The system implements self-service by automatically collecting historical scene data, calculating similarities between current and historical scenes, and autonomously determining which startup resources to load. The system uses attribute comparison (time, location, user behavior) and similarity algorithms to make intelligent decisions without requiring manual intervention, thereby improving prediction accuracy while managing complexity through automated processes
Data Source
AI summary
An electronic device includes a memory and processing circuitry. The memory is to be loaded with resources for applications to be executed at the electronic device. The processing circuitry obtains a current data group having attributes for a current running scene. Further, the processing circuitry obtains historical data groups respectively corresponding to a plurality of historical scenes. A historical data group of the historical data groups includes corresponding attributes to the attributes for a historical running scene. Then, the processing circuitry calculates similarities respectively for the historical data groups to the current data group, and determines a historical scene from the plurality of historical scenes based on the similarities. In addition, the processing circuitry determines a potential application for the current running scene. The potential application was executed in the determined historical scene. Then, the processing circuitry loads a resource for the potential application into the memory.