Network Analytics Tool Dashboard Recommendation Engine
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Solution Overview
Problem
Current network analytics systems face challenges in processing vast amounts of data from various sources, correlating key performance indicators (KPIs) across multiple dimensions, and effectively recommending relevant dashboards to users amidst a complex and dynamic environment.
Innovation Solution
The proposed solution involves a method for a network analytics tool that analyzes time-varying parameter values and generates user-specific dashboard recommendations by learning user interests and behaviors, incorporating content importance scores, and adapting to changing user profiles and data patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system processes an enormous amount of data from various sources and creates numerous dashboards to cover all possible KPI combinations, then the comprehensiveness of network monitoring is improved, but the complexity of the system and the difficulty of selecting relevant dashboards increases
Solution Approach 1:
The system automatically performs data processing, correlation, and dashboard generation without requiring manual intervention. The analytics engine autonomously processes enormous amounts of data from multiple sources, creates numerous dashboards covering all KPI combinations, and recommends relevant dashboards to users based on their profiles and behaviors, thereby reducing manual complexity while maintaining comprehensive monitoring coverage
Solution Approach 2:
The system dynamically adjusts dashboard recommendations based on changing parameters such as user profiles, behaviors, and network conditions. The recommendation engine modifies dashboard selections in real-time based on user interactions, time of day, network events, and other variables, allowing the system to adapt to varying requirements without increasing structural complexity
2Adaptability or versatility
If the system creates a large number of dashboards to cover diverse network dimensions and KPI combinations, then the coverage of monitoring capabilities is improved, but the ease of operation decreases as users cannot check all relevant dashboards manually
Solution Approach 1:
The system continuously collects feedback from user interactions with dashboards and uses this information to refine future recommendations. The recommendation engine analyzes user behaviors, time spent on dashboards, and interaction patterns to improve the accuracy of dashboard selections, creating a feedback loop that enhances both monitoring coverage and operational ease over time
Solution Approach 2:
The recommendation engine acts as an intermediary between the vast number of available dashboards and the user. Instead of requiring users to manually navigate through numerous dashboards, the system intelligently selects and presents only the most relevant dashboards based on user profiles, current network conditions, and historical behaviors, significantly improving ease of operation while maintaining comprehensive monitoring capabilities
3Speed
If the system provides real-time analytics and dashboard recommendations, then the speed of response is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary processing and pre-computation of network data and dashboard configurations in advance. The analytics engine continuously processes and correlates data from multiple sources, pre-calculates KPI aggregations, and prepares dashboard templates before actual user requests occur. This allows the system to respond rapidly to user interactions and network events without requiring intensive computational resources at the moment of request
Solution Approach 2:
The system applies partial processing strategies by focusing computational efforts on the most relevant data and dashboards rather than processing everything uniformly. The recommendation engine identifies and processes only the necessary portions of data based on user profiles and current context, reducing overall computational resource consumption while maintaining fast response speeds for the selected dashboards
4Measurement precision
If the system learns user interests and behaviors to provide personalized recommendations, then the relevance of dashboard suggestions is improved, but the complexity of user profile analysis increases
Solution Approach 1:
The system automatically performs user profile analysis and dashboard recommendation generation without requiring manual configuration. The recommendation engine autonomously analyzes user behaviors, interactions, and preferences to create personalized profiles and generate relevant dashboard suggestions, eliminating the need for complex manual profile setup while maintaining high recommendation relevance
Solution Approach 2:
The system replaces manual user profile configuration with automated machine learning-based analysis. Instead of requiring users to manually define their preferences and roles, the system uses algorithms to automatically learn user interests and behaviors from interaction data, time patterns, and dashboard usage histories, reducing the complexity of profile management while improving recommendation precision
Data Source
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AI summary
The present disclosure provides a method for a network analytics tool capable of analyzing a time-varying set of parameter values for each of a plurality of measurable parameters and visually outputting a plurality of visualization objects. Each of the visualization objects comprise one or more visualization items, each visualization item representative of one of the plurality of measurable parameters. The method comprises: obtaining, for each of the plurality of visualization objects, a value of a first object-related metric and causing generation of a sensory-perceptible output indicative of a visualization object ranking established on the basis of the obtained value of the first object-related metric of each of the plurality of visualization objects. Obtaining a value of the first object-related metric includes, for each of one or more of the plurality of visualization objects, determining a value of the first object-related metric based at least on the set of parameter values of the measurable parameter of each visualization item included in the visualization object.