Geolocation Accuracy via Reverse DNS Hostname Analysis
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Solution Overview
Problem
Existing IP geolocation databases often lack accuracy, leading to incorrect location determinations for users, which can result in dissatisfaction with search engine services and impact industries like credit card fraud protection, content delivery, and e-commerce, affecting user retention and revenue.
Innovation Solution
A geolocation database generation system that uses a machine learning approach to extract and disambiguate IP addresses by consulting their reverse DNS hostnames, training classifiers with geographical features and ground truth data to improve location determination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IP geolocation databases are used to determine user location, then location determination can be performed without GPS or user input, but the accuracy of location determination is insufficient
Solution Approach 1:
The patent combines multiple data sources including reverse DNS hostnames, IP geolocation databases, and machine learning classification to determine user location. By merging these complementary approaches, the system achieves both the automation of IP-based lookup and the accuracy of hostname-based geographical extraction, resolving the contradiction between convenience and precision.
Solution Approach 2:
The patent introduces reverse DNS hostnames as an intermediary element between IP addresses and geographical locations. Instead of directly mapping IP addresses to locations, the system uses hostnames as a mediating layer that contains embedded geographical information, thereby improving accuracy while maintaining the automated IP-based determination process.
2Measurement precision
If reverse DNS hostnames are used to extract geographical information, then location accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service by training a machine learning classifier to automatically extract and classify geographical information from reverse DNS hostnames. The system trains on labeled data and then autonomously processes new hostnames without requiring manual intervention, thereby improving accuracy while managing complexity through automation rather than manual processes.
Solution Approach 2:
The patent performs preliminary action by pre-training the machine learning classifier on labeled geographical data before deployment. This pre-processing step creates a ready-to-use model that can quickly and accurately classify hostnames, reducing the computational complexity during actual location determination while maintaining high accuracy.
3Measurement precision
If machine learning classification is implemented to process hostnames, then location accuracy is enhanced, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning classifier offline on comprehensive labeled datasets. This upfront investment in training creates a optimized model that can then rapidly classify new hostnames with high accuracy, shifting the computational burden from real-time processing to offline preparation and thereby reducing actual query processing time.
Data Source
AI summary
Generating an improved/more accurate geolocation database is provided. Given a dataset of reverse DNS hostnames for IP addresses, ground truth information, and a hierarchical geographical database, a machine learning classifier can be trained to extract and disambiguate location information from the reverse DNS hostnames of IP addresses and to apply machine learning algorithms to determine location candidates and to select a most probable candidate for a reverse DNS hostname based on a confidence score. The classifier can be used to generate an accurate geolocation database, or to provide accurate geolocation information as a service.