Adaptive Event Processing for Selective UAR Enrichment
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Solution Overview
Problem
Conventional UARs lack sufficient event details for detailed troubleshooting, leading to insufficient information in some cases and excessive data volume in others, especially with the increased demands of 5G networks, limiting root-cause analysis capabilities.
Innovation Solution
The solution dynamically generates and evaluates UARs, identifying normal and abnormal UARs, forwarding a subset of normal UARs with reduced data and appending enrichment data to abnormal UARs for detailed analysis, thereby reducing overall data volume while enhancing troubleshooting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed event data is collected for all telecommunication sessions, then troubleshooting capability is improved, but data volume and system resource load increase excessively
Solution Approach 1:
The patent applies local quality by providing different levels of data detail to different UARs based on their characteristics. Normal UARs receive minimal processing with reduced data volume, while abnormal UARs receive full enrichment with complete event details. This differentiated approach ensures troubleshooting information is available where needed (for abnormal sessions) while avoiding excessive data collection for normal sessions.
Solution Approach 2:
The patent implements preliminary action by evaluating UARs and identifying abnormal sessions before detailed enrichment is applied. The system pre-processes UARs by checking against threshold conditions and failure event criteria, then selectively enriches only those UARs that require detailed analysis. This preliminary filtering prevents unnecessary data processing and reduces overall data volume.
2Quantity of substance
If minimal data is collected for normal UARs, then data volume is reduced, but troubleshooting information may be insufficient
Solution Approach 1:
The system applies local quality by selectively providing detailed information only where required. Normal UARs are processed with minimal data collection, while abnormal UARs identified through evaluation receive complete enrichment with all relevant event details. This ensures information sufficiency is maintained for troubleshooting cases without unnecessarily collecting data for normal operations.
Solution Approach 2:
The system performs preliminary evaluation of each UAR against threshold conditions and failure criteria before determining the level of data collection. This preliminary action identifies which UARs require detailed troubleshooting information, ensuring that sufficient information is available for abnormal sessions while minimizing data collection for normal sessions.
3Loss of information
If all generated UARs are processed for analytics, then analysis completeness is improved, but system resource consumption increases
Solution Approach 1:
The patent applies the extraction principle by removing unnecessary UARs from the analytics processing queue. The system extracts and identifies abnormal UARs through evaluation, then selectively processes only these enriched UARs for detailed analytics. This extraction of relevant cases from the total UAR set maintains analysis completeness for problematic sessions while reducing overall system resource consumption.
Solution Approach 2:
The system implements partial action by processing a subset of UARs rather than all generated records. Through selective enrichment and filtering, the system processes only abnormal UARs that require detailed analysis, performing partial processing that is sufficient for troubleshooting purposes while avoiding the excessive resource consumption of processing every UAR in full detail.
4Loss of information
If enrichment data is appended to all UARs, then troubleshooting capability is improved, but data volume and processing complexity increase
Solution Approach 1:
The patent applies local quality by appending enrichment data selectively only to abnormal UARs identified through evaluation. Normal UARs are processed without enrichment, while abnormal UARs receive complete event detail supplementation. This localized application of enrichment maintains troubleshooting capability for problematic sessions while avoiding unnecessary processing complexity for normal sessions.
Solution Approach 2:
The system performs preliminary evaluation of each UAR against threshold conditions and failure criteria before appending enrichment data. This preliminary action identifies which UARs require detailed troubleshooting information, ensuring that enrichment is applied only where necessary. This approach maintains complete troubleshooting information for abnormal sessions while reducing overall data processing complexity.
Data Source
AI summary
The dynamically generation and evaluation of User Activity Records (UARs) is presented herein to determine which UARs to forward for analytics processing, and how much information to include with the forwarded UARs. To that end, UARs are identified as normal, e.g., those UARs satisfy an evaluation condition, e.g., a threshold condition, and or as abnormal, e.g., those UARs that do not satisfy an evaluation condition, e.g., the threshold condition. For those UARS identified as normal, only a small subset of the normal UARs are forwarded for further analysis to reduce the data volume associated with these normal UARs. For those UARs identified as abnormal, enrichment data is appended to the generated UAR to generate a detailed UAR, all which is forwarded for further analysis.