Mainframe Data Analytics via JCL Compiler Intermediary
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Solution Overview
Problem
Integrating data analytics into mainframe environments is complex and costly, as direct querying of mainframe systems is resource-heavy and requires significant MIPS, making it difficult for entities to meet evolving business needs.
Innovation Solution
A system comprising a processing device that determines data analytics resources, initiates compiler protocols, establishes communication links with JCL subsystems, generates job control statements, and executes them on the mainframe environment, incorporating engines for natural language processing, vectorization, normalization, and optimization to facilitate data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct querying is used to analyze mainframe data, then data analysis capability is achieved, but computing cost and complexity increase significantly
Solution Approach 1:
The patent introduces a data analytics resource layer as an intermediary between the mainframe environment and analytics tools. This layer includes compiler protocols that translate analytics operations into mainframe-executable code, and a communication link layer that interfaces with JCL subsystems. This intermediary structure enables data analytics capability while shielding users from mainframe complexity and reducing direct querying costs.
Solution Approach 2:
The system segments the data analytics function into distinct resources: data analytics resources for processing, compiler protocols for code generation, communication links for data transfer, and job control statements for execution. This segmentation allows each component to be optimized independently and simplifies the overall integration architecture, reducing complexity while maintaining productivity.
2Adaptability or versatility
If data analytics resources are integrated into mainframe environment, then business needs are met, but integration complexity increases
Solution Approach 1:
The data analytics resources are designed with multi-functionality to serve various business needs through a unified interface. The compiler protocols can generate different types of executable code for various analytics operations, and the communication link layer handles multiple data transfer scenarios. This universality enables the system to meet diverse business requirements without proportionally increasing integration complexity.
Solution Approach 2:
The intermediary layers (compiler protocols and communication links) act as mediators that abstract the mainframe environment's complexity from the analytics tools. This allows analytics resources to be integrated with mainframe systems without requiring deep knowledge of mainframe internals, thereby reducing integration complexity while maintaining adaptability to business needs.
3Ease of operation
If compiler protocols are initiated to build executable code, then code execution on mainframe is enabled, but processing time increases
Solution Approach 1:
The compiler protocols perform preliminary compilation of analytics code into executable format before execution on the mainframe. By preparing the executable code in advance through the compiler layer, the actual execution time on the mainframe is reduced, as the code is already optimized for the mainframe architecture. This preliminary action enables ease of operation while minimizing time loss.
Data Source
AI summary
Systems, computer program products, and methods are described herein for implementing data analytics in a mainframe environment. The present invention is configured to determine one or more data analytics resources associated with natural language processing algorithms; initiate one or more compiler protocols on the one or more data analytics resources to build one or more executable code for the one or more data analytics resources capable of being executed on a mainframe environment; establish a communication link with a job control language (JCL) subsystem associated with the mainframe environment; transmit the one or more executable code for the one or more data analytics resources to the JCL subsystem; generate one or more job control statements configured to be executable on the mainframe environment; generate a log of the one or more job control statements; and initiate an execution of the one or more job control statements on the mainframe environment.