Recommendation Engine for Interconnection Facility Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interconnection facility providers face challenges in efficiently recommending optimal interconnection and co-location strategies for customers across geographically distributed facilities, as existing methods lack comprehensive data-driven approaches to identify the most suitable interconnection opportunities based on customer profiles and service affinities.
Innovation Solution
A recommendation engine is employed to analyze telemetry data and generate scoring data for prospective interconnection facilities by identifying sets of facilities with existing interconnections among customers, considering compatibility criteria such as industry, services, and customer profiles, to provide intelligent interconnection and co-location planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recommendation engine analyzes telemetry data to identify optimal interconnection facilities, then the quality of interconnection recommendations is improved, but the complexity of the system increases
Solution Approach 1:
A recommendation engine is introduced as an intermediary component that analyzes telemetry data and generates scoring data for prospective interconnection facilities. This intermediary processes complex data relationships between customers and facilities, transforming raw telemetry information into actionable recommendations without requiring direct complex interactions between all system components.
Solution Approach 2:
The patent replaces manual or rule-based recommendation methods with an automated recommendation engine that uses telemetry data analysis. This substitution transitions from mechanical/manual processes to an automated system that dynamically generates recommendations based on real-time data, improving precision while managing complexity through automation.
2Loss of information
If scoring data is generated for multiple prospective interconnection facilities, then the ability to make informed decisions is improved, but the time required for analysis increases
Solution Approach 1:
The recommendation engine performs preliminary analysis by pre-calculating scoring data for multiple prospective interconnection facilities based on telemetry data. This preliminary action prepares recommendation scores in advance, so when customers need to make decisions, the analysis is already completed, reducing the time required at the decision-making moment while maintaining comprehensive information quality.
3Measurement precision
If the recommendation engine considers multiple compatibility criteria including industry, services, and customer profiles, then the accuracy of interconnection recommendations is improved, but the computational complexity increases
Solution Approach 1:
The recommendation engine segments the compatibility assessment into distinct criteria categories: industry compatibility, services compatibility, and customer profile compatibility. Each criterion is evaluated separately using telemetry data, allowing the system to manage computational complexity by breaking down the overall assessment into manageable segments while maintaining high accuracy through comprehensive multi-criteria evaluation.
Data Source
AI summary
In some examples, a method includes obtaining, by a recommendation engine executing at a computing device, a list of prospective interconnection facilities administered by an interconnection facility provider and a list of prospective interconnection facility customers; identifying, by the recommendation engine, based at least in part on querying telemetry data that indicates interconnections of interconnection facility customers within the list of prospective interconnection facilities, sets of interconnection facilities from the list of prospective interconnection facilities in which respective interconnection facility customers are configured with existing interconnections; generating, by the recommendation engine and based at least in part on the list of prospective interconnection facilities, the sets of interconnection facilities, and the list of prospective interconnection facility customers, scoring data for the prospective interconnection facilities; and outputting, by the recommendation engine and for display to a user, the scoring data.