Real-Time Mobile Network User Analytics via Application Data Correlation
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Solution Overview
Problem
Current methods fail to provide transparent, real-time, multi-dimensional user-level visibility into mobile data network usage, limiting mobile operators' ability to optimize networks and content providers' capacity for targeted advertising, due to limitations in data collection and analysis across various dimensions.
Innovation Solution
A monitoring platform that collects and stores application-level activity data, correlating it with user information such as location, demographics, and device type, using IP address-mobile phone number pairings to provide dynamic, real-time reporting and analytics, enabling detailed visibility for both service providers and content providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mobile operators implement comprehensive data collection and analysis systems to gain user-level visibility into network usage, then network optimization capability and user experience improvement are enhanced, but system complexity and implementation difficulty increase significantly
Solution Approach 1:
The patent introduces a data collection and analysis system as an intermediary component that sits between the mobile network infrastructure and the operators. This system captures usage data from multiple network points, processes it through centralized analysis modules, and delivers processed insights to operators. The intermediary handles the complexity of data aggregation, correlation, and analysis internally, while presenting simplified user-level visibility reports to operators, thus resolving the contradiction between comprehensive information access and system complexity.
2Measurement precision
If the system collects and processes detailed application-level activity data in real-time, then reporting accuracy and multi-dimensional analysis capability improve, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data processing and aggregation at multiple stages. Usage data is collected and pre-processed at network access points before being transmitted to the central analysis system. The system pre-aggregates data by user, application, and time dimensions, and pre-computes baseline metrics. This preliminary action reduces the volume and complexity of data requiring real-time processing, enabling accurate multi-dimensional reporting without excessive processing delays.
Solution Approach 2:
The data processing system is segmented into multiple independent modules that handle different aspects of data analysis simultaneously. Collection modules capture data from various network points, processing modules analyze different data dimensions in parallel, and reporting modules generate various reports concurrently. This segmentation enables the system to process detailed application-level activity data with high accuracy across multiple dimensions without creating a single processing bottleneck, thus reducing overall data processing time.
3Duration of action of stationary object
If the monitoring platform implements comprehensive data storage for predetermined periods, then historical analysis and trend detection capabilities improve, but storage requirements and system resource consumption increase
Solution Approach 1:
The patent implements a hierarchical storage architecture where different types of data are stored with different retention periods and detail levels based on their analytical value. Recently accessed and high-value user-level data is stored with full detail for shorter periods, while aggregated and summarized data is retained longer with reduced detail. The system applies local quality optimization by storing comprehensive data only where and when needed for specific analysis purposes, rather than uniformly across all data, thus reducing overall storage requirements while maintaining historical analysis capabilities.
Data Source
AI summary
A method and apparatus for storing data on application-level activity and other user information to enable real-time multi-dimensional reporting about a user of a mobile data network. A data manager receives information about application-level activity from a mobile data network and stores the information to provide dynamic real-time reporting on network usage. The data manager comprises a database, data processing module, and analytics module. The database stores the application-level data for a predetermined period of time. The data processing module monitors the data to determine if it corresponds to a set of defined reports. If the data is relevant, the processing module updates the defined reports. The analytics module accesses the database to retrieve information satisfying operator queries about network usage. If the operator chooses to convert the query into a defined report, the analytics module creates a newly defined report and populates it accordingly.