Multi-namespace Testing Data Analysis for IT Asset Triage
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Solution Overview
Problem
Conventional automated triage services face inefficiencies in analyzing testing data for IT assets, as they often require combining global and time-sensitive data into a single namespace, leading to redundant processing, resource-intensive procedures, and inaccuracies due to misinterpretation of patterns across different analysis windows.
Innovation Solution
Implementing multiple namespaces for analyzing testing data, where global-level analysis generates partial results that are inserted into individual test suite namespaces, allowing each suite to account for overall scenario attributes without the need for redundant data combination, thereby streamlining the analysis process and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global and time-sensitive data are combined into a single namespace for analysis, then comprehensive analysis results can be obtained, but redundant processing and resource consumption increase
Solution Approach 1:
The patent divides the analysis system into multiple independent namespaces (global namespace and time-sensitive namespaces) that process different portions of testing data separately. Each namespace maintains its own analysis context and generates partial results independently, eliminating the need to combine all data into a single namespace and process everything together, thus reducing redundant operations and improving processing efficiency.
Solution Approach 2:
The global namespace performs preliminary analysis on global testing data first, generating partial results that are then inserted into time-sensitive namespaces. This preliminary action allows time-sensitive analyses to start with pre-processed information already in place, reducing the overall processing time and resource consumption while maintaining comprehensive analysis coverage.
2Measurement precision
If all testing data are processed in a single namespace, then complete analysis coverage is achieved, but resource consumption and processing time increase
Solution Approach 1:
The patent segments testing data into different portions (global data and time-sensitive data) and processes them in separate namespaces simultaneously. This parallel processing approach maintains complete analysis coverage by ensuring all data portions are analyzed, while significantly reducing total processing time compared to sequential processing in a single namespace.
Solution Approach 2:
Multiple namespaces operate continuously and independently to process different data portions without waiting for other namespaces to complete. The system maintains continuous useful action across all namespaces, with results being integrated at the end, thereby reducing overall processing time while ensuring complete analysis coverage.
3Measurement precision
If data from different analysis windows are combined, then comprehensive pattern recognition is possible, but misinterpretation of patterns occurs
Solution Approach 1:
The patent maintains separate namespaces for different analysis windows (global and time-sensitive), preserving the contextual integrity of each window's data. By analyzing data within its original context rather than mixing it with data from other windows, the system avoids misinterpretation of patterns while still achieving comprehensive pattern recognition through the integration of results from multiple namespaces.
4Measurement precision
If redundant data combination procedures are implemented, then complete analysis results are generated, but system complexity and maintenance difficulty increase
Solution Approach 1:
The patent structure divides the analysis system into independent namespaces that each handle specific data portions with their own processing logic. This segmentation eliminates the need for complex data combination procedures, as each namespace produces results independently that are then integrated. The modular architecture reduces system complexity and makes maintenance easier compared to a monolithic system that requires redundant data combination.
Data Source
AI summary
An apparatus comprises a processing device configured to obtain a testing data set associated with a test scenario comprising a configuration phase and execution of test suites, the testing data set comprising a first portion generated from the configuration phase and additional portions generated from execution of the test suites. The processing device is also configured to generate a first data structure based on analyzing the first portion of the testing data set in a first namespace, and to insert at least a portion of the first data structure into additional namespaces each comprising a corresponding one of the additional portions of the testing data set generated from execution of one of the test suites. The processing device is further configured to generate additional data structures based on analyzing the additional portions of the testing data set and the inserted components of the first data structure in the additional namespaces.