Adaptive IP Location Assignment via Dynamic Map Scoring
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Solution Overview
Problem
Current systems face challenges in accurately and adaptively assigning locations to internet protocol (IP) indexed data streams, especially with partially observable data streams, due to dynamic IP allocations, partial data visibility, and the complexity of merging data from multiple sources, which affects scalability and accuracy in geolocation estimation.
Innovation Solution
An adaptive location assignment method using machine learning that generates and updates IP-to-location and location-to-IP maps based on historical data, scores entries by frequency and uniqueness, filters entries, and predicts real-time locations within active Spatio-Temporal regions, incorporating Bayesian updating and Kalman filtering to handle dynamic and missing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If public IP allocation is used for location estimation, then network address availability is improved, but location accuracy deteriorates due to dynamic and ephemeral IP re-allocation
Solution Approach 1:
The system dynamically updates IP-to-location mappings in real-time based on incoming data streams, adapting to changing IP allocations. The location estimation model continuously learns from new data, adjusting to ephemeral IP re-allocations without requiring static mappings, thereby maintaining accuracy despite dynamic network conditions.
Solution Approach 2:
The system incorporates feedback loops where location estimation results are continuously refined based on incoming data streams. The model uses feedback from multiple data sources to correct and improve location predictions, compensating for the inherent inaccuracy of dynamic IP allocations through iterative refinement.
2Quantity of substance
If multiple data sources are merged for location estimation, then data coverage is improved, but system complexity deteriorates due to partial visibility and data overlap
Solution Approach 1:
The system merges multiple data streams from different sources into a unified location estimation model. By combining GPS data, IP-based location, and other location indicators into a single probabilistic framework, the system achieves comprehensive data coverage while managing complexity through integrated processing rather than separate handling of each source.
Solution Approach 2:
The system changes parameters by transforming multiple data sources into a unified probabilistic representation. Instead of processing each data source separately, the model converts diverse inputs into probability distributions over possible locations, simplifying the merging process through parameter transformation and normalization.
3Measurement precision
If constant updating of IP to location mapping is performed, then location accuracy is improved, but processing time deteriorates due to partial data availability
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing IP-to-location mappings and location-to-IP mappings in advance. Historical mapping data is maintained and updated asynchronously, allowing real-time location estimation to leverage pre-computed information rather than calculating everything from scratch, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The system applies partial action by updating only the necessary portions of the mapping data based on incoming streams. Instead of reprocessing all data constantly, the model selectively updates mappings where changes are detected, reducing processing overhead while maintaining current accuracy for all locations through targeted updates.
Data Source
AI summary
Disclosed is a method of adaptive assignment of location to internet protocol (IP) indexed data streams using machine learning from partially observable location data. The method includes (i) generating an IP to location map (NM1) and a location to IP map (NM2) by (i) mapping the IP address from location indexed data streams to corresponding location temporally and vice versa, in a base map, (ii) scoring and ranking each entry in the NM1 and the NM2 based on a frequency of use of the IP address, and a number of unique entity identifiers per IP address or per location, (iii) filtering each entry in the NM1 and the NM2 to generate a dynamic IP to location map, and (iv) estimating, a location for an IP indexed data streams at multiple levels of resolutions based on the dynamic IP to location map and an active ST region of each entity.