Network Address Location Mapping via Clustering
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Solution Overview
Problem
Existing systems face challenges in accurately determining representative location information for network addresses due to rapid changes in location data from mobile devices, leading to outdated mappings in databases.
Innovation Solution
A service provider server processes transaction information, filters out cellular data connections, applies clustering criteria to determine representative location information, and uses a third-party database if necessary to ensure accurate mapping, storing associations in a second database with an accuracy measure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network address to location mappings are stored in databases using mobile device communications, then location information can be obtained for transaction processing, but the mappings become outdated due to rapid changes in location data and network address re-assignment
Solution Approach 1:
The system pre-calculates representative location information for network addresses by clustering location data before transactions occur. This preliminary clustering creates stable location mappings that anticipate future queries, ensuring that when transactions need location data, accurate and pre-processed information is already available without requiring real-time database updates.
Solution Approach 2:
The system dynamically determines whether to calculate representative location information based on clustering criteria or use third-party databases. The approach adapts to changing conditions by evaluating whether sufficient location data exists for clustering, and switching between self-calculated and externally-sourced location information based on data availability and quality.
2Measurement precision
If representative location information is calculated using clustering criteria, then accurate location mapping is achieved, but system complexity increases due to filtering and clustering processes
Solution Approach 1:
The system segments the location determination process into distinct phases: filtering cellular data connections, clustering location information, determining representative locations, and storing results. This segmentation allows each component to be optimized independently and enables the system to handle complex processing through modular, manageable steps rather than a monolithic complex process.
Solution Approach 2:
The system introduces a representative location calculation mechanism as an intermediary between raw location data and transaction processing. This intermediary layer clusters location information and determines representative locations, simplifying the data structure for downstream transactions while maintaining accuracy. The intermediary absorbs the complexity of clustering algorithms, presenting a simplified interface to the rest of the system.
3Measurement precision
If cellular data connections are filtered out before clustering, then location accuracy improves by excluding mobile devices, but data loss occurs by removing potentially valid location information
Solution Approach 1:
The system applies different quality criteria to different types of location data based on their characteristics. Cellular data connections are filtered out at the local level before clustering because mobile devices exhibit rapidly changing locations that don't represent stable network address mappings. This local quality filtering ensures that only location data with appropriate stability characteristics contribute to representative location calculations.
Solution Approach 2:
The system changes the parameter of data inclusion by filtering on connection type (excluding cellular) before clustering. This parameter change transforms the dataset from including all location data to including only stable location data from non-cellular connections, fundamentally altering the clustering input to produce more reliable representative locations for network addresses.
Data Source
AI summary
A method is provided that includes accessing, by a server provider server of a service provider, a database storing associations between network addresses and locations. Additionally, the method includes determining a subset of the database corresponding to a first network address, each association included in the subset corresponding to an association between the first network address and a respective location. The method also includes in response to determining that the subset of the database satisfies one or more clustering criteria, calculating a representative location corresponding to the first network address, and storing an association between the first network address and the representative location in a second database.