Dynamic Local POI Caching by Location and User Interest
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Solution Overview
Problem
Mobile devices face the challenge of being overloaded with irrelevant point-of-interest (POI) data based on user location and interests, leading to inefficient data management and increased response times for geographical searches.
Innovation Solution
A system that maintains a local POI database on mobile devices by caching geodescriptors based on proximity and user preferences, using proximity requirements, access ranges, geographical access areas, and reciprocal bearing access angles to selectively add and remove POIs, ensuring only relevant data is stored and accessible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a mobile device stores a comprehensive POI database, then the completeness of POI data is improved, but the device memory usage and data processing time increase
Solution Approach 1:
The POI database is segmented into multiple hierarchical levels: a comprehensive master database stored on servers, a smaller local cache database on mobile devices, and dynamically loaded POI subsets based on user context. This segmentation allows the system to maintain data completeness on servers while providing fast local access to frequently needed POIs, resolving the contradiction between data completeness and search speed.
Solution Approach 2:
The system performs preliminary actions by pre-caching POI data that is likely to be needed based on user profile, historical behavior, and predicted location. The local database is proactively populated with relevant POIs before the user actually needs them, reducing wait time during geographical searches while maintaining a manageable local database size.
2Ease of operation
If a mobile device caches all POI data locally, then data accessibility is improved, but the device becomes overloaded with irrelevant data
Solution Approach 1:
The system applies local quality by customizing the local POI cache content according to each user's specific needs, preferences, and context. Different users receive different subsets of POI data tailored to their interests (e.g., restaurants for food enthusiasts, museums for culture lovers). This ensures high data relevance for each user while keeping the local database size manageable, preventing overload with irrelevant information.
Solution Approach 2:
The system dynamically changes parameters such as cache size limits, POI selection criteria, and data retention policies based on user profile attributes, historical behavior patterns, and current context. These parameter adjustments optimize the balance between data accessibility and relevance, ensuring the local cache contains the right amount of relevant POI data for each user without becoming overloaded.
3Adaptability or versatility
If the system provides personalized POI filtering, then user experience is improved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary layer (server-side processing and algorithms) that handles the complex tasks of POI filtering, personalization, and cache management. The mobile device itself remains relatively simple, relying on the server to perform sophisticated analysis of user profiles and generate personalized POI subsets. This intermediary approach enables advanced personalization capabilities while keeping device complexity manageable.
Solution Approach 2:
The system implements self-service mechanisms where the POI database automatically updates and reorganizes itself based on changing user preferences and behavior patterns. The caching system autonomously determines what data to retain or evict from local storage without requiring manual user intervention. This self-managing approach reduces operational complexity while maintaining high adaptability to user needs.
Data Source
AI summary
A local object database is populated from a remote object records database. An object has a geolocation representing one or more locations relative to points and/or regions relative to a geographic range of object records. A method might comprise determining a device geolocation state of the mobile device, determining a first geographical search space as a function of device geolocation and range threshold relative to the device geolocation, querying the remote database for a localized subset of object records with geolocations within the first geographical search space, populating the local database with the search results, determining whether the results comprise filterable object records that include a filter parameter, comparing the filter parameter and device geolocation state. When a filterable object record is present in the results and a filter parameter does not match the device geolocation state, the filterable object record is filtered from the local database.