Network Activity Data File Difference Identification
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Solution Overview
Problem
Current network troubleshooting systems face difficulties in accurately identifying differences in network activity data files due to unrelated changes and varying network conditions, making it challenging to isolate changes caused by specific network adjustments.
Innovation Solution
A system and method that compare activity data files by matching tier pairs, performing statistical and protocol analysis, and rendering differences in a user-friendly format, allowing users to categorize and filter results based on customizable templates, providing a graphical user interface for intuitive analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct comparison of trace files is performed, then all differences are identified, but unrelated differences from network condition variations complicate the task of identifying changes attributable to enacted changes
Solution Approach 1:
The patent segments the network activity data into structured fields (timestamp, source IP, destination IP, protocol, etc.) and compares these segmented components systematically. This allows isolation of specific change attributes from unrelated variations, improving identification accuracy while managing complexity through structured analysis.
Solution Approach 2:
The patent introduces an intermediary comparison mechanism that filters and mediates between the two trace files by establishing correspondence relationships between records. This intermediary layer helps distinguish causal differences from spurious variations, reducing the complexity of direct comparison.
2Ease of operation
If statistical information is calculated and displayed in comparison reports, then differences are presented in a comparative manner, but users must know where to look and how to decipher differences in complex traces
Solution Approach 1:
The patent employs visual differentiation (analogous to color changes) by marking and highlighting differences between trace files in the comparison report. This visual encoding makes differences immediately recognizable and easy to locate, eliminating the need for users to manually search through complex data while maintaining clear interpretation of what constitutes a meaningful difference.
3Measurement precision
If manual observation and operations are used to discern differences, then detailed analysis is possible, but the process is time-consuming and lacks automation
Solution Approach 1:
The patent implements self-service automation where the system automatically performs the comparison analysis, generates correspondence relationships, and produces difference reports without requiring manual intervention. This automated self-service approach maintains high detection precision through systematic algorithmic comparison while dramatically improving productivity by eliminating manual analysis time.
Data Source
AI summary
A method of identifying differences between activity data files includes determining a difference between the activity data files. Causal analysis may be performed to identify a cause of the difference. The difference and/or the cause of the difference may be rendered based on a rendering template. Tier pairs between the activity data files may be matched and a user may be queried to confirm the tier pair match. Statistical and/or protocol differences between each of the activity files may be presented. Transactions between each of the activity data files may be matched including comparing the content files in each of the activity data files that account for the transactions. Client side differences between each of the activity data files may be identified. A categorization may be assigned to each of the determined differences. Determined differences may be excluded from the rendering.