JSON Template Diagnostic System for Cable Modem Data
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Solution Overview
Problem
Existing diagnostic tools for cable modem termination systems struggle to efficiently process and identify meaningful correlations within large volumes of diagnostic data from cable modems, often producing non-useful granular data or failing to identify useful correlations due to the variability and flexibility of JSON-formatted data.
Innovation Solution
A JSON template-based diagnostic system is employed to process and match relevant data characteristics, ignoring irrelevant JSON portions and focusing on name-value pairs and hierarchical structures to identify matching data within diagnostic data sets, allowing for the extraction of meaningful diagnostic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing diagnostic tools process large volumes of diagnostic data from cable modems, then the quantity of processed data increases, but the ability to identify meaningful correlations deteriorates due to data variability and JSON format flexibility
Solution Approach 1:
The diagnostic data processing system segments the large volume of JSON-formatted diagnostic data into structured components using templates. Each template defines specific data elements and their hierarchical relationships, breaking down the unstructured data stream into manageable, meaningful units that can be analyzed for correlations without being overwhelmed by the total data volume.
Solution Approach 2:
The system changes the parameter of data representation by transforming flexible JSON formats into standardized template-matched structures. By imposing a rigid template structure on variable JSON data, the system converts unstructured information into a consistent format where correlations can be systematically identified, effectively changing how the data is organized and processed.
2Measurement precision
If diagnostic tools focus on granular data details, then measurement precision increases, but the ability to identify useful correlations deteriorates due to excessive detail and noise
Solution Approach 1:
The template-based system extracts only the relevant data elements defined in the template from the complete diagnostic data set. By selectively taking out and processing only the template-matched portions, the system maintains precision in measuring those specific elements while filtering out extraneous granular details that would otherwise obscure meaningful correlations.
Solution Approach 2:
Rather than processing every piece of granular data, the system applies partial action by focusing only on the data elements specified in the template. This selective processing avoids the excessive detail problem while maintaining sufficient precision for the diagnostic purpose, as the template defines exactly which data points require accurate measurement.
3Quantity of substance
If diagnostic tools process all JSON-formatted data without filtering, then completeness of data processing improves, but processing efficiency deteriorates due to irrelevant data portions
Solution Approach 1:
The template acts as an intermediary between the raw JSON diagnostic data and the analysis process. It mediates by defining which portions of the JSON data are relevant, allowing the system to process complete and accurate information while filtering out irrelevant portions through the template matching mechanism, thus maintaining both completeness and efficiency.
4Adaptability or versatility
If diagnostic tools handle varied JSON formats, then adaptability improves, but system complexity increases due to format variability and matching requirements
Solution Approach 1:
The template-based system provides a universal processing framework that handles multiple JSON formats through a single mechanism. The same template-matching approach works across different JSON structures and formats, making the system adaptable to various data sources while maintaining consistent processing logic, thereby reducing overall system complexity despite handling diverse formats.
Data Source
AI summary
A method of processing diagnostic data including receiving a template file and a diagnostic file. Processing the diagnostic data based upon the template file where array and non-array portions of the data are treated differently.