Compressed Critical File Conversion for Faster Reliable Access
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Solution Overview
Problem
Existing systems face issues with compressed critical files causing system outages and data corruption due to additional processing overhead, unprintable characters, and manual pack checking processes, which are time-intensive and difficult to predict.
Innovation Solution
A system and method that uses automated detection and conversion of compressed data sets to an uncompressed format using particle swarm optimization (PSO), monitoring data set formats, and utilizing a knowledge base to identify and convert highly utilized critical data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If data is saved in packed (compressed) format, then storage space is reduced, but processing overhead increases causing system outages and increased response time
Solution Approach 1:
The system proactively identifies compressed data sets before they cause problems and converts them to uncompressed format in advance. This preliminary action prevents the processing overhead issues from occurring during normal system operation, resolving the contradiction by eliminating the need to process compressed data while maintaining storage efficiency for non-critical data.
Solution Approach 2:
The system applies different data formats to different data sets based on their specific characteristics and usage patterns. Critical data sets that require fast processing are converted to uncompressed format, while non-critical data sets can remain compressed. This local differentiation resolves the contradiction by optimizing each data set's format according to its specific needs rather than applying a universal format.
2Volume of stationary object
If ISPF algorithm compresses data, then storage efficiency improves, but data corruption occurs with unprintable characters and incorrect line breaks
Solution Approach 1:
The system performs preliminary identification of ISPF-compressed data sets using detection mechanisms that recognize compression patterns before data access or transfer operations. By detecting and converting these data sets to uncompressed format in advance, the system prevents data corruption issues from occurring during subsequent operations, thus resolving the contradiction between compression efficiency and data integrity.
3Reliability
If manual pack checking is performed, then data verification is possible, but time consumption and processing burden increase significantly
Solution Approach 1:
The system replaces manual pack checking with automated detection mechanisms that use algorithms to identify compressed data sets. This substitution eliminates the need for time-consuming manual verification while maintaining detection accuracy, resolving the contradiction between verification reliability and time consumption by using automated computational methods instead of manual processes.
Solution Approach 2:
The system enables data sets to self-identify their compression status through built-in detection patterns and markers. Each data set essentially checks itself for compression characteristics, eliminating the need for external manual verification. This self-service approach maintains verification accuracy while dramatically reducing the time and processing burden associated with manual checking.
4Volume of stationary object
If ISPF packed format is used, then space savings are achieved, but file transfer results in corrupted files with unusable characters
Solution Approach 1:
The system performs preliminary detection of ISPF-compressed data sets before file transfer operations and converts them to uncompressed format in advance. This preliminary action ensures that files are in the correct format for transfer, preventing corruption and unusable characters from appearing in transferred files, thus resolving the contradiction between space savings and transfer reliability.
Data Source
AI summary
A computer-implemented method includes: monitoring, by a computing device, a data set format of all files in a computing system to determine a list of data sets in a first format which are accessed greater than a predetermined number of times; gathering, by the computing device, the list of data sets in the first format within the computing system; and converting, by the computing device, the list of data sets in the first format within the computing system to a second format which is different from the first format.