Hierarchical Tile Synchronization via Composite Cluster Hashing
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Solution Overview
Problem
Existing tile-based mapping systems face inefficiencies in determining optimal tile sizes and organizing tiled images for efficient storage and retrieval, leading to slow rendering and caching issues due to the use of large, randomly sized source images and inefficient data access methods.
Innovation Solution
A multi-processor, memory-efficient system that determines the most efficient tile size by calculating the number of tiles and bytes needed for various map views, and organizes tiled images using a data organization scheme that includes generating random map views, calculating tile sizes, and storing tiles in a cluster file structure with fixed-length indices for efficient access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tile-based mapping systems use multiple discrete zoom levels with fixed map scales, then map views can be virtualized using multiple image tiles with discrete addressing, but determining the most efficient tile size for a specific purpose becomes complex and time-consuming
Solution Approach 1:
The system pre-calculates and stores optimal tile sizes for different zoom levels and map views before actual rendering occurs. By determining efficient tile sizes in advance based on predefined criteria (such as power-of-2 dimensions for computational efficiency), the system avoids complex real-time calculations during rendering, thus improving productivity while managing complexity through upfront preparation.
Solution Approach 2:
The system establishes standardized tile size parameters (e.g., 256x256, 512x512 pixels) that can be systematically changed based on zoom level and rendering requirements. These parameter changes follow mathematical relationships (such as doubling tile dimensions at each zoom level) that simplify the determination process while optimizing rendering efficiency across different scales.
2Quantity of substance
If large source images are used to cover extensive geographic areas, then fewer tiles are needed, but rendering and downloading becomes slower and consumes more memory
Solution Approach 1:
The system segments large source images into smaller, uniformly sized tiles that can be independently processed, cached, and transmitted. This segmentation allows the rendering system to load and process only the specific tiles needed for the current map view rather than handling entire large images, significantly reducing rendering time and memory consumption while maintaining the ability to cover extensive geographic areas through composition of multiple tiles.
Solution Approach 2:
The system transitions from thinking in terms of large 2D image dimensions to a 3D tile space dimensionality, where tiles are organized by zoom level, row, and column coordinates. This additional organizational dimension enables efficient addressing and retrieval of specific tiles without requiring the entire image to be loaded into memory, thus reducing both rendering time and memory usage.
3Ease of manufacture
If tile images are stored on a server system and sent to client systems with minimal processing, then processing is done ahead of time, but inefficient data access methods lead to slow retrieval
Solution Approach 1:
The system pre-processes source images into standardized tile formats with consistent dimensions and coordinate systems before storage. Tiles are organized in hierarchical directory structures or database indexes that enable direct access by tile coordinates. This preliminary organization allows the server to retrieve specific tiles using direct indexing rather than scanning or searching, dramatically improving retrieval speed while maintaining the principle of minimal processing during actual rendering.
Solution Approach 2:
The system introduces an intermediary indexing layer between the stored tile images and the retrieval process. This index structure (such as hash tables, B-trees, or coordinate-based file systems) acts as a mediator that translates requested map coordinates into direct tile file paths or database keys, enabling O(1) or O(log n) retrieval time instead of linear scanning, thus improving speed without adding processing complexity to the tile format itself.
4Adaptability or versatility
If random source images of different sizes are used to create tile sets, then flexibility is maintained, but determining optimal tile size and organizing tiles for efficient storage becomes difficult
Solution Approach 1:
The system applies standardized parameter transformations to source images of varying sizes, scaling them to predetermined tile dimensions (such as 256x256 or 512x512 pixels). This parameter standardization allows tiles to be uniformly organized in storage structures with predictable patterns, enabling efficient indexing and retrieval. The adaptability to handle different source image sizes is maintained through automated scaling operations, while the storage organization benefits from the regularity of uniform tile parameters.
Data Source
AI summary
A system and method for hierarchical synchronization of tiles between a first dataset and a second dataset by computing and storing multiple composite cluster hash values for multiple clusters in the first and second dataset. For each of the composite cluster hash values, comparing a composite cluster hash value of a particular cluster in the first dataset with a composite cluster hash value of a corresponding cluster in the second dataset. When the composite cluster hash values of the corresponding clusters do not match, retrieving all tile hashes corresponding to all the tiles from the non-matching cluster in the first dataset, and retrieving all tile hashes corresponding to all the tiles from the non-matching cluster in the second dataset. Finally, determining the one or more changed tiles between the non-matching cluster in the first dataset and the non-matching cluster in the second dataset by comparing the corresponding tile hashes.