Intelligent Mapping of Empirical Data for Software Function Analysis
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Solution Overview
Problem
Software systems generate abundant and detailed data during execution, making it challenging to identify which functions are in use while maintaining confidentiality and facilitating further processing.
Innovation Solution
The method involves collecting data on system events, using a data consumption program to retrieve and map building blocks, and determining higher-level functionality through intelligent mapping, allowing for the identification of active functions without exposing sensitive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed and abundant data is collected during software execution, then information about system events is improved, but data confidentiality and ease of analysis deteriorate
Solution Approach 1:
The patent segments detailed system event data into standardized building blocks with specific data elements (program name, module name, function name, etc.). This segmentation transforms abundant raw data into structured, analyzable units that maintain information integrity while reducing analysis complexity through systematic organization.
Solution Approach 2:
The patent introduces building blocks as an intermediary layer between raw system event data and high-level functionality analysis. These building blocks serve as a mediator that structures detailed data into standardized formats, enabling easier mapping to functional information while preserving data confidentiality through abstraction.
2Measurement precision
If detailed data is collected during software execution, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
Detailed system event data is segmented into standardized building blocks containing specific data elements (program name, module name, function name, data set name, etc.). This segmentation maintains measurement precision by preserving detailed information while improving ease of operation through structured organization that enables systematic processing and analysis.
Solution Approach 2:
The patent transforms detailed raw data into building blocks with standardized parameters and data elements. This parameter change from unstructured detailed data to structured building blocks with defined attributes maintains measurement precision while significantly improving ease of operation through consistent formatting and organized data structures.
3Loss of information
If building blocks are mapped to identify functions, then functionality identification is improved, but data confidentiality deteriorates
Solution Approach 1:
Building blocks serve as an intermediary that enables functionality identification while protecting data confidentiality. The standardized building block structure allows mapping to high-level functions through abstraction, extracting functional information without exposing sensitive detailed data, thus resolving the conflict between information gain and confidentiality preservation.
Solution Approach 2:
The patent creates simplified copies of detailed system event data in the form of standardized building blocks. These building block copies contain essential information for function identification while abstracting away sensitive details, enabling functionality analysis without compromising data confidentiality through the use of representative rather than complete data copies.
Data Source
AI summary
Embodiments of the present invention provide systems and methods for performing data analysis. Mapping analytics are applied on data which contains extensive information. Mapped building blocks are found by applying mapping analytics. These mapped building blocks help determine which functions are in use within a system. By determining which functions are in use within a system, a higher-level of functionality in use can also be determined.