Scheduled Job Execution Management via Data Integrity Verification
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Solution Overview
Problem
Current systems for managing scheduled jobs lack the ability to monitor the integrity and timeliness of data sets, leading to inefficient and resource-wasting re-executions due to errors or incomplete data, especially in multi-processing environments where data accuracy is not verified before execution.
Innovation Solution
A scheduled job management system that tracks the status of data sets generated by remote processing devices, using checklists and status indicators to delay or prevent the execution of jobs until accurate data is available, and notifies administrators of potential issues to ensure data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scheduled jobs are executed at predetermined times without data verification, then system productivity is maintained, but data integrity deteriorates leading to incorrect processing results
Solution Approach 1:
The system performs preliminary verification of data set status before executing scheduled jobs. The job management system checks whether required data sets are complete and accurate prior to job execution, preventing incorrect processing results while maintaining execution timing.
Solution Approach 2:
The system implements feedback mechanisms by monitoring data set status and providing information about data completeness to the job scheduling system. This feedback loop enables the system to make informed decisions about job execution based on current data availability and quality.
2Reliability
If manual monitoring of data integrity is implemented, then data accuracy improves, but system complexity increases
Solution Approach 1:
The job management system automatically monitors and verifies data set status without requiring manual intervention. The system self-manages the complexity of data integrity verification by implementing automated checking mechanisms that continuously track data availability and quality.
Solution Approach 2:
The job management system acts as an intermediary between data storage systems and job execution systems. It mediates the interaction by verifying data integrity before job execution, absorbing the complexity of monitoring while presenting a simple interface to users.
3Reliability
If jobs are re-executed due to incomplete data, then data completeness improves, but processing resources are wasted
Solution Approach 1:
The system checks data completeness before job execution to prevent wasted re-executions. By verifying that all required data sets are complete and accurate prior to starting job processing, the system avoids consuming processing resources on jobs that would fail or produce incorrect results.
Solution Approach 2:
The system provides feedback about data set status to prevent unnecessary job executions. When data is incomplete or inaccurate, the system notifies users and prevents job execution, avoiding waste of processing resources while ensuring data completeness is addressed.
4Productivity
If automated job execution is implemented, then productivity increases, but ability to respond to data issues decreases
Solution Approach 1:
The automated job management system incorporates feedback mechanisms that notify users of data issues while maintaining automated execution for routine operations. When data problems are detected, the system provides alerts and information to users, allowing manual intervention when necessary while preserving automation for normal operations.
Data Source
AI summary
Scheduled jobs can be managed by monitoring the veracity of the data sets processed by these jobs. A multi-processing environment includes remote processing devices generating the data sets. A central processing device executes scheduled job, also known as batch processes, using the data sets. For each of the scheduled jobs, a scheduled job management device tracks the data set generated by the remote processing devices. Through tracking these data sets, the execution of the scheduled jobs may be managed. If one or more of the data sets are not the proper data for the scheduled job, the management device may thereupon control the execution of the job. Through the utilization of the management device, the execution of improper scheduled jobs may be avoided.