Wireless Hotspot to POI Mapping via Iterative Probability Propagation
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Solution Overview
Problem
Conventional methods for mapping wireless hotspots and points of interest (POIs) rely on name-based correlations, which are often irrelevant in practical scenarios due to the lack of similarity between hotspot and POI names.
Innovation Solution
A method and apparatus that utilize sniffing records, sniffing device overlap degrees, and distance-based initial mapping probabilities to iteratively propagate and establish target mapping probabilities between wireless hotspots and POIs, thereby creating a more accurate and relevant mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If name-based mapping method is used to map wireless hotspots and POIs, then the mapping process is simple, but the mapping accuracy is poor due to lack of relevance between hotspot names and POI names
Solution Approach 1:
The patent introduces POI category as an intermediary element to bridge the gap between wireless hotspot names and POI names. Instead of directly matching names, the system uses category labels (e.g., restaurant, hotel, retail) as a mediator to establish indirect associations, enabling accurate mapping even when names have no semantic relationship
Solution Approach 2:
The patent transforms the mapping problem from name-based string matching to a multi-parameter classification problem. By incorporating POI category, distance, and user behavior data as multiple parameters, the system achieves accurate mapping through comprehensive parameter analysis rather than relying on single name similarity
2Device complexity
If conventional name-based mapping is used, then the system complexity is low, but the mapping reliability is poor due to irrelevant names
Solution Approach 1:
The patent creates a universal mapping framework that handles multiple mapping scenarios (different POI types, various hotspot naming conventions, different geographic regions) through a unified approach. The POI category-based method serves as a universal solution that works across diverse situations where name-based methods fail
Solution Approach 2:
POI category acts as a reliable intermediary that ensures consistent and accurate mapping across different scenarios. The category system provides a standardized classification layer that mediates between variable hotspot names and POI data, ensuring high reliability regardless of naming conventions
3Measurement precision
If iterative propagation method is used to establish mapping probabilities, then the mapping accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing POI category associations and distance relationships before iterative propagation. This preprocessing step creates a structured foundation that guides the iterative process, reducing the search space and computational burden while maintaining accuracy
Solution Approach 2:
The iterative propagation process incorporates feedback mechanisms where mapping probabilities are continuously refined based on sniffing device overlap degrees and distance information. Each iteration uses feedback from previous results to adjust and improve probability estimates, converging to accurate mappings through progressive refinement
Data Source
AI summary
This disclosure relates to a method and an apparatus for mapping wireless hotspots and points of interest (POIs), a computer-readable storage medium, and a computer device. The method includes: obtaining sniffing records, each of the sniffing records including data of wireless hotspots sniffed by sniffing devices; determining sniffing device overlap degrees between the wireless hotspots according to the sniffing records; determining, according to distances between the wireless hotspots and POIs, initial mapping probabilities between the wireless hotspots and the POIs; performing iterative propagation among the initial mapping probabilities based on the sniffing device overlap degrees, and obtaining target mapping probabilities between the wireless hotspots and the POIs when the iteration ends; and establishing a mapping between the wireless hotspots and the POIs according to the target mapping probabilities.