Selective Mainframe SMF Record Capture for Big Data
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Solution Overview
Problem
Mainframe data is often underutilized in big-data analytics due to indiscriminate collection and processing of large volumes of records, leading to inefficiencies and missed opportunities for critical insights, as existing systems struggle to selectively capture and integrate mainframe operating system records at the same rate they are produced.
Innovation Solution
A computer-based method that selectively captures mainframe records by parsing SMF records, applying predetermined criteria to extract relevant field values, and converting them into a compatible format for integration with other business data, minimizing delivery cycle time and avoiding unnecessary data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If indiscriminate collection of mainframe data is performed, then completeness of data capture is improved, but processing power and storage capacity requirements increase significantly
Solution Approach 1:
The patent extracts only the relevant subset of mainframe SMF records that meet predetermined capture criteria, rather than collecting all mainframe data. The system identifies and extracts specific field values from SMF records based on selection criteria, converting only the necessary data to target format for integration with business data, thereby reducing processing and storage requirements while maintaining data completeness for analytical purposes
Solution Approach 2:
The patent applies different quality standards to different portions of mainframe data by implementing selective capture criteria. Not all SMF records are processed with the same level of detail - instead, the system applies local quality filtering to capture only those records and fields that meet specific business relevance thresholds, optimizing resource allocation based on data value rather than uniform processing
2Productivity
If selective capture of mainframe records is implemented, then processing efficiency is improved, but delivery cycle time may increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining capture criteria, field mappings, and target format specifications before mainframe data arrives. The system pre-configures selection rules and conversion parameters, so that when SMF records are generated, they can be rapidly filtered and transformed without ad-hoc processing delays, maintaining near-real-time delivery while improving processing efficiency
Solution Approach 2:
The patent replaces mechanical batch processing with an automated event-driven system that triggers on mainframe SMF record generation. Instead of periodic bulk extraction, the system substitutes a continuous automated monitoring and capture mechanism that processes records as they are produced, reducing delivery cycle time while maintaining selective capture efficiency
3Loss of information
If all mainframe SMF records are captured and converted, then data completeness for analysis is improved, but storage capacity requirements increase
Solution Approach 1:
The patent extracts only the essential subset of SMF record fields that are relevant to business analytics, excluding redundant or low-value data. By applying selection criteria to identify and extract only necessary field values, the system reduces the volume of captured data while preserving data completeness for analytical purposes, directly addressing storage capacity constraints
Data Source
AI summary
A computer-based method includes receiving, at a compute device, a collected set of system measurement facility (SMF) data from a mainframe operating system. The method includes, retrieving from a memory operatively coupled to the compute device, a set of SMF field-type identifiers. Each SMF field-type identifier having a one-to-one logical relation with a conditional value from a set of conditional values. The method includes selecting SMF field values from the collected set of SMF data, based on the set of conditional values. The SMF field values is a subset of SMF field values included in the collected set of SMF data. The method further includes executing a flattening process to produce a flattened record that includes at least one flattened SMF field value converted into a target format, and inserting the flattened record into a data repository compatible with the target format.