Point-of-Interest Merging via Graph Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer-implemented technologies face challenges in accurately and efficiently identifying matching points-of-interest across different datasets due to large data volumes and variations in information, leading to inefficient merging processes in mapping systems.
Innovation Solution
A point-of-interest merging system generates a graph data structure with weighted feature relationships between data records, clusters these records into groups, and creates merged records based on the clusters, improving matching accuracy and efficiency by utilizing batch processing, matching, clustering, and merging facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automatic identification methods are used to identify matching points-of-interest across different datasets, then the process can be automated, but the accuracy and efficiency deteriorate due to large data volumes and variations in information
Solution Approach 1:
The patent segments the matching process into multiple stages: initial filtering using graph data structures to identify candidate matches, followed by more detailed comparison and verification. This multi-stage segmentation allows automated processing while maintaining high accuracy by applying different levels of analysis to different subsets of data.
Solution Approach 2:
The patent introduces graph data structures as an additional dimensional approach to the matching problem, representing points-of-interest and their relationships as nodes and edges. This dimensional transformation enables efficient automated traversal and comparison while capturing complex relationships that traditional flat data structures miss, thereby improving both automation capability and matching accuracy.
2Quantity of substance
If traditional methods process large amounts of point-of-interest data from multiple sources, then comprehensive coverage is achieved, but processing efficiency deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing data into graph data structures before the actual matching operation. This includes organizing points-of-interest into hierarchical categories, pre-computing relationships, and establishing indexing structures. These preliminary actions enable efficient processing of large datasets during the matching phase without sacrificing comprehensive coverage.
Solution Approach 2:
The patent introduces graph data structures as an intermediary representation layer between the raw multi-source datasets and the matching algorithm. This intermediary structure efficiently aggregates and organizes data from multiple sources while maintaining relationships, allowing the system to process comprehensive data volumes with improved efficiency through graph traversal and query optimization.
3Measurement precision
If detailed comparison is performed on all point-of-interest records to ensure accurate matching, then matching precision improves, but processing time increases
Solution Approach 1:
The patent segments the comparison process into hierarchical levels: first comparing high-level graph structure properties (nodes, edges, relationships), then progressively comparing more detailed attributes only for candidate matches. This segmentation enables the system to quickly eliminate non-matching records through coarse-grained comparison while applying detailed precision comparison only where necessary, thereby maintaining high precision with reduced processing time.
Data Source
AI summary
An illustrative computing system generates, based on a plurality of point-of-interest data records each including data representative of a distinct set of features for a distinct point-of-interest associated with a distinct geographic location, a graph data structure that includes the plurality of point-of-interest data records and a plurality of weighted feature relationships interconnecting the plurality of point-of-interest data records. The computing system generates one or more merged point-of-interest data records based on the graph data structure and provides the one or more merged point-of-interest data records to a mapping system to indicate one or more points-of-interest on a user interface map of a geographic area.