Context-Based Data Pre-Fetching for Mobile Applications
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Solution Overview
Problem
Mobile computing platforms face challenges in providing users with a full-featured experience due to resource restrictions, leading to insufficient data on devices and connectivity issues, which hinder the functionality of mobile applications.
Innovation Solution
A context-based system and method for determining the necessary data on a mobile device by creating a context model that considers device and user-specific variables, updating context variables, and using inference engines to pre-fetch and notify mobile applications of the required data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is pre-determined and downloaded to mobile devices before disconnection, then data availability on mobile devices is improved, but device memory and processing resources are consumed
Solution Approach 1:
The system performs preliminary actions by predicting which data will be needed before the mobile device disconnects from the network. The prediction engine analyzes user context, application state, and historical patterns to pre-fetch and download relevant data subsets while connected, making data available offline without loading all possible data onto the device.
Solution Approach 2:
The system dynamically adjusts the data subset downloaded to the mobile device based on changing conditions. The prediction engine continuously monitors user context, application state, and network conditions to adaptively determine what data to pre-fetch, optimizing the balance between data availability and device resource consumption.
2Quantity of substance
If a subset of enterprise data is downloaded to mobile devices, then device memory consumption is reduced, but data completeness and user needs fulfillment deteriorate
Solution Approach 1:
The system performs preliminary prediction of user information needs before disconnection occurs. By analyzing the current application state, user context, and usage patterns, the prediction engine identifies and pre-fetches the specific subset of data that will be needed, ensuring data completeness for predicted needs while minimizing overall data transfer.
Solution Approach 2:
The system changes the parameters of data selection by using predictive analytics to determine which data attributes and records are most relevant. Instead of using fixed or static criteria for data subset selection, the system dynamically adjusts selection parameters based on predicted user needs, application requirements, and contextual factors.
3Measurement precision
If mobile users manually determine necessary data, then data selection precision is improved, but user time and operational complexity increase
Solution Approach 1:
The system provides self-service by automatically predicting and determining the data subset needed without requiring manual user intervention. The prediction engine autonomously analyzes user context, application state, and usage patterns to identify necessary data, eliminating the time and effort users would spend manually determining their data needs.
Solution Approach 2:
The system uses feedback loops to continuously improve data selection accuracy. By monitoring actual user interactions, application usage patterns, and prediction outcomes, the system refines its predictive models over time, improving the accuracy of future data subset predictions while maintaining automated operation.
4Device complexity
If the traditional request/response paradigm is used in mobile environments, then system simplicity is maintained, but reliability deteriorates due to connectivity issues
Solution Approach 1:
The system performs preliminary data pre-fetching and local caching before network disconnection occurs. By anticipating user needs and downloading necessary data while connected, the system enables offline application functionality without requiring complex real-time synchronization mechanisms or continuous network connectivity.
Solution Approach 2:
The system introduces a prediction engine and local data cache as intermediaries between the user and enterprise servers. This intermediary layer enables offline operation by serving predicted data needs from local storage, reducing direct dependency on network connectivity while maintaining a relatively simple overall architecture.
Data Source
AI summary
Methods, systems, and computer program products for context-based data pre-fetching and notification for applications are described herein. In an embodiment, the method operates by creating a context model that includes context variables and events. The method populates context variables based upon the context of an application and instantiates a context based upon the context model. The method determines whether the context is active or inactive and infers a likely set of data needed by the application. The method executes a data selection function to generate a dataset for the application. In an embodiment, the system includes a module to create and maintain a context model. The system includes modules to: populate context variables within the context model; calculate a dataset for the application; maintain an inference engine; subscribe to changes in the context variables; and generate notifications including a dataset with associated metadata that assists with display of the dataset.