Mainframe Data Flow Analysis Across Jobs and Field Layouts
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Solution Overview
Problem
Current data flow analysis solutions for mainframe applications are limited in their ability to perform comprehensive field-level analysis across multiple granularity levels, failing to identify all possible data flow paths and differentiate between different record layouts, leading to inefficiencies in modernization and maintenance.
Innovation Solution
A computer-implemented method that performs field-level data flow analysis at multiple granularity levels, including program, job, and job scheduler levels, by analyzing mainframe application source code to identify all possible data flow paths and record layouts, even for schema-less datasets, using a knowledge graph to track data transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive field level data flow analysis is performed across multiple granularity levels and all possible data flow paths, then measurement precision and analysis completeness are improved, but device complexity and computational resources required increase
Solution Approach 1:
The patent segments the data flow analysis into multiple granularity levels (program level, job level, job scheduler level) and processes them systematically. Each level analyzes specific aspects of data flow, with program level examining individual programs, job level examining sequences of programs, and job scheduler level examining job execution sequences. This segmentation enables comprehensive analysis while managing complexity through structured progression.
Solution Approach 2:
The patent introduces multiple dimensions of analysis by transitioning from program level to job level to job scheduler level. This dimensional expansion allows the system to analyze data flow paths across different organizational layers, capturing comprehensive field level details while maintaining manageable complexity through hierarchical structuring.
2Loss of information
If all possible data flow paths between datasets are analyzed, then analysis completeness is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by first establishing the hierarchical structure of programs, jobs, and job schedulers before conducting the data flow analysis. This preliminary organization enables systematic traversal of all possible data flow paths while optimizing the search process through pre-established relationships and control flow chains.
Solution Approach 2:
The patent maintains continuity of useful action by systematically progressing through program level analysis, then job level analysis, and finally job scheduler level analysis without interruption. Each level builds upon the previous level, ensuring that all possible data flow paths are examined while maintaining efficient sequential processing.
Data Source
AI summary
Data flow analysis is provided. A program level data flow analysis is performed for each respective data flow path in a set of control flow chains corresponding to each respective program called by a particular job step in each respective job of a plurality of jobs in a sequence of job execution corresponding to an application. A particular field of a plurality of fields is identified in a record of each dataset of a plurality of datasets corresponding to the application that is included in a particular data flow path to form a field level data flow analysis for each particular data flow path. Results of the field level data flow analysis for each particular data flow path in the set of control flow chains corresponding to each respective program called by the particular job step in each respective job of the plurality of jobs is aggregated.