Offline Map Update Deltas Using Quadtree-Based Compression
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Solution Overview
Problem
Existing systems for updating offline map data on mobile devices are inefficient in terms of data storage, network bandwidth usage, and processing cycles, as they require storing and updating large amounts of data while maintaining accuracy and usability.
Innovation Solution
A system that uses a server device to organize map data into a quadtree structure, generating update data using binary and semantic difference algorithms to determine the smallest compressed size, and sends only the necessary updates to client devices, optimizing compression and update efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional offline map data update methods are used, then map data can be updated on mobile devices, but data storage space, network bandwidth usage, and processing cycles are excessively consumed
Solution Approach 1:
The patent segments map data into hierarchical levels (Level 0 to Level 3) and organizes it using a quadtree structure. This segmentation allows the system to transmit only the necessary portions of map data at appropriate detail levels, rather than transmitting entire map datasets. The quadtree divides geographic space into hierarchical quadrants, enabling selective transmission of only those regions that have changed or are currently needed, significantly reducing data storage requirements while maintaining update accuracy.
Solution Approach 2:
The patent extracts and transmits only the differential updates (changes) from the server rather than complete map datasets. By using difference algorithms to identify and extract only the modified portions of map data, the system minimizes network bandwidth usage and data storage requirements on mobile devices while ensuring that map data remains current and accurate.
2Adaptability or versatility
If complete map data is downloaded and stored on mobile devices, then offline map usage is enabled, but network bandwidth and data storage are excessively consumed
Solution Approach 1:
The patent segments map data into hierarchical levels and geographic regions using a quadtree structure, allowing mobile devices to store only the specific geographic regions and detail levels that are currently needed for offline usage. This segmentation enables the system to provide offline map functionality while minimizing network bandwidth consumption by downloading only necessary portions of map data rather than complete datasets.
Solution Approach 2:
The patent implements partial action by transmitting and storing only the necessary portions of map data required for offline functionality, rather than downloading complete map datasets. The system determines the minimum required data based on user location, zoom level, and predicted movement patterns, thereby enabling offline map usage while significantly reducing network bandwidth usage and energy consumption.
3Reliability
If frequent map data updates are performed, then map data remains current, but processing cycles and time required for updates increase
Solution Approach 1:
The patent segments map data into a quadtree hierarchy with multiple levels of detail, allowing the system to perform incremental updates on specific nodes rather than processing entire map datasets. This segmentation enables frequent updates to maintain data currency while improving processing efficiency by only reprocessing affected regions and levels rather than the complete map data set.
Solution Approach 2:
The patent implements preliminary action by pre-processing and organizing map data into a quadtree structure on the server side before transmission. This preliminary organization of data into hierarchical nodes and regions allows for more efficient client-side updates, as the mobile device receives pre-processed differential data that requires minimal additional processing to apply, thereby maintaining map data currency while reducing update processing time and cycle consumption.
4Area of stationary object
If large amounts of map data are transmitted to mobile devices, then comprehensive offline coverage is provided, but compressed data size and transmission time increase
Solution Approach 1:
The patent segments map data into a quadtree hierarchy that divides the coverage area into hierarchical quadrants at multiple levels. This segmentation allows the system to transmit data in manageable chunks corresponding to specific geographic regions and detail levels, providing comprehensive offline coverage while minimizing the total transmitted data volume by sending only the necessary regions at appropriate detail levels rather than transmitting complete high-resolution datasets for all areas.
Data Source
AI summary
In some implementations, a system can optimize offline map data updates. For example, a server device in the system can determine a metric for identifying map data objects based on attributes of the map data objects. The server device can then generate a quadtree that stores the map data objects in nodes of the quadtree based on the metric. When processing an update to the map data stored at the server device, the server device can generate update data describing the updates for each node in the quadtree based on a binary difference algorithm and/or a semantic difference algorithm. The server device can select the algorithm based on which algorithm results in the smallest compressed size of the update data.