Hash-Based Geospatial Vector Feature Synchronization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Synchronizing large geospatial vector feature datasets across distributed systems is inefficient due to the need for full dataset resending, which is time-consuming and bandwidth-intensive, especially with suboptimal data connections.
Innovation Solution
Implementing hash-based synchronization using geohashes, feature hashes, and layer hashes to identify and selectively synchronize only modified features between source and destination datastores, reducing the amount of data transferred by partitioning geographic areas and using hash comparisons to determine changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire geospatial vector feature dataset is resent to users for updates, then all users receive complete data, but bandwidth consumption increases and update time extends
Solution Approach 1:
The patent extracts only the modified vector features from the source dataset and transmits them to destination systems, rather than transmitting the entire dataset. This is achieved by comparing layer hashes to identify changes and then selectively synchronizing only those specific features that have been modified, thereby reducing bandwidth consumption while maintaining data completeness at the destination.
Solution Approach 2:
The patent segments the geospatial vector feature dataset into individual vector features, each with its own hash identifier. By segmenting the data at this granular level, the system can identify and transmit only the specific modified features rather than the entire dataset, resolving the contradiction between data completeness and bandwidth efficiency.
2Reliability
If the entire geospatial vector feature dataset is physically shipped to users, then all users receive complete data, but update time increases especially for widely distributed users
Solution Approach 1:
The patent extracts and transmits only the modified vector features rather than the entire dataset, significantly reducing the volume of data that needs to be physically shipped or transmitted to widely distributed users. This extraction approach maintains data completeness at the destination while dramatically reducing update time.
Solution Approach 2:
The patent performs preliminary hash comparisons at the source system to identify modified features before transmission. By pre-processing the data and determining exactly what needs to be synchronized, the system avoids transmitting unnecessary data, thereby reducing physical shipment times and update delays for distributed users.
3Reliability
If users with limited internet bandwidth receive full dataset downloads, then they obtain complete data, but the download process becomes impractical
Solution Approach 1:
The patent extracts only the modified vector features for transmission to users with limited bandwidth. By identifying changes through hash comparison and transmitting solely those modified features rather than the entire dataset, the system makes data synchronization feasible for users with constrained internet bandwidth while ensuring they receive all necessary updates.
4Loss of energy
If hash-based synchronization is implemented to transfer only modified features, then bandwidth consumption reduces, but system complexity increases
Solution Approach 1:
The patent introduces hash values as an intermediary mechanism to manage the complexity of synchronization. By computing and comparing layer hashes and feature hashes, the system automatically identifies modified features without requiring complex manual synchronization logic. This intermediary hashing approach simplifies the overall system architecture while achieving efficient bandwidth utilization.
Data Source
AI summary
Embodiments relate to hash-based synchronization of geospatial vector features. Initially, a list of spatial data layers in a source spatial datastore is obtained. For each of the spatial data layers, a source layer hash is determined for a source data layer of the spatial data layers, a destination data layer is identified in a destination spatial datastore that is related to the source data layer, where the destination data layer is associated with a destination layer hash, and in response to determining that the source layer hash and the destination layer hash do not match, source features from the source data layer are selectively synchronized to the destination data layer.