Intelligent Data Retrieval and Local Caching for Mobile Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In enterprise computing environments, accessing large amounts of distributed data can lead to network bottlenecks and inefficient retrieval of relevant information, especially on mobile devices with small screens, where it is impractical for humans to sift through millions of data entries to identify critical data points.
Innovation Solution
A system and method for intelligent data retrieval and caching, utilizing a local data store closer to the user device, which predicts and preempts data storage based on user interactions, and uses machine learning to determine relevant metrics and metrics values, reducing network traffic and enabling real-time high-fidelity data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in centralized data sources, then data completeness is improved, but network bottleneck and access speed deteriorate
Solution Approach 1:
The patent segments the centralized data storage into multiple distributed data sources across different geographical locations. Each data source stores a portion of the overall data, allowing users to access relevant data from the nearest location, thereby reducing network bottlenecks while maintaining data completeness through the distributed network.
Solution Approach 2:
The patent introduces a spatial dimension to data storage by distributing data across multiple geographical locations. This transforms the traditional single-point access model into a multi-dimensional access structure where data can be retrieved from any location in the network, improving access speed without sacrificing data completeness.
2Loss of information
If all data entries are displayed, then information completeness is improved, but usability and readability deteriorate
Solution Approach 1:
The patent extracts and highlights only the most relevant data points from the complete dataset based on user interactions and predefined criteria. Instead of displaying all millions of data entries, the system selectively presents critical information while maintaining access to the complete dataset, thereby improving usability without sacrificing information completeness.
Solution Approach 2:
The patent applies local quality by customizing the display of data based on specific user needs, device characteristics, and interaction patterns. Different users see different subsets of data tailored to their roles and requirements, making the information both complete in scope and optimized for individual usability.
3Measurement precision
If data is accessed in real-time from remote sources, then data fidelity is improved, but response time and network load deteriorate
Solution Approach 1:
The patent implements preliminary action by pre-fetching and caching data that is likely to be needed based on user interaction patterns and predictive algorithms. This allows the system to provide real-time data fidelity when needed while reducing the frequency of remote data source accesses, thereby maintaining accuracy without incurring the associated network delays and loads.
4Device complexity
If data storage locations are fixed, then system simplicity is improved, but adaptability to user location deteriorates
Solution Approach 1:
The patent implements dynamics by making data storage and access patterns adaptive to user location and device characteristics. The system dynamically determines which data sources to access based on real-time user context, balancing the simplicity of fixed storage locations with the need for location-aware data delivery.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for intelligent data retrieval, caching, and display. In some embodiments, the system generates a (GUI) in response to a request from a user device. The system may receive the location of the user device and identification of an initiative from the user device. The system may identify a metric correlated with the initiative based on an analysis of previously received initiatives. The system may identify and retrieve a value of the metric from a data source storing a plurality of metrics. The system may store the metric value at a local data store closer in proximity to the user device than the plurality of metrics at the data source. The system may then generate an updated graphical user interface (GUI) with the initiative, metric, and metric value.