Hierarchical Metadata Comparator for Telecommunications Network Analysis
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Solution Overview
Problem
Comparing large amounts of telecommunications network data to benchmark data to identify similarities or differences is challenging, and effectively visualizing these differences is difficult.
Innovation Solution
A hierarchical comparator tool, such as the scenario, call, and Protocol Data Unit (PDU) comparator, that allows users to compare capture files, call records, call scenarios, and PDUs in both visual and command line modes, with options to discard non-significant information and apply tolerances, enabling graphical comparison and automatic validation of regression tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of network data are compared to benchmark data to identify similarities or differences, then the completeness of data analysis is improved, but the complexity of the comparison process and difficulty of visualization increase
Solution Approach 1:
The patent segments network data into hierarchical levels (capture files, call traces, call records, PDUs) and compares data at each level separately. This segmentation allows comprehensive data analysis while managing complexity by breaking down the comparison process into manageable hierarchical stages rather than comparing all data simultaneously.
Solution Approach 2:
The patent introduces a hierarchical dimension to the comparison process, organizing data into multiple levels of abstraction. This dimensional approach transforms the complex problem of comparing large data volumes into a structured multi-level process, where each level can be visualized and analyzed independently, reducing overall complexity.
2Measurement precision
If large amounts of network data are compared to benchmark data to identify similarities or differences, then the completeness of data analysis is improved, but the difficulty of visualization increases
Solution Approach 1:
The patent segments network data into hierarchical levels (capture files, call traces, call records, PDUs) and compares data at each level separately. This segmentation allows comprehensive data analysis while managing complexity by breaking down the comparison process into manageable hierarchical stages rather than comparing all data simultaneously.
Solution Approach 2:
The patent introduces a hierarchical dimension to the comparison process, organizing data into multiple levels of abstraction. This dimensional approach transforms the complex problem of comparing large data volumes into a structured multi-level process, where each level can be visualized and analyzed independently, reducing overall complexity.
3Measurement precision
If packet-by-packet comparison is performed on capture files, then the precision of comparison is improved, but the processing time and productivity decrease
Solution Approach 1:
The patent segments the comparison process into hierarchical levels, performing comparisons at aggregate levels (capture file level, call trace level, call record level) before drilling down to packet level only when necessary. This segmentation enables fast preliminary comparisons that filter out matching cases without requiring detailed packet-by-packet analysis.
Solution Approach 2:
The patent applies partial comparison by performing detailed packet-level comparison only on specific portions of data that require it, rather than comparing all packets uniformly. The system performs aggregate-level comparisons first and only applies excessive detailed action where needed, improving overall processing efficiency.
Data Source
AI summary
A system, method and computer program product monitors the operation of a telecommunications network and receives source metadata at a metadata comparator. The source metadata is associated with data captured from a source in a telecommunications network. Target metadata associated with target data is also received at the metadata comparator. The source and target metadata are compared to identify metadata parameters that match or do not match. Bias data is also received at the metadata comparator. The bias data comprises weighting parameters and/or tolerance parameters. The weighting and tolerance parameters correspond to selected metadata parameters.


