Batch Graph Localization for SLA Time Deviation Fixes
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Solution Overview
Problem
Existing batch processing systems face challenges in accurately predicting and addressing time deviations in complex batch processes due to large volumes of jobs and complex interdependencies, leading to unexpected delays and outages, with existing predictive solutions often requiring manual effort and lacking end-to-end transparency.
Innovation Solution
A method and system that utilize a batch graph to localize jobs contributing to time deviations by applying an iterative bottom-up traversal approach, identifying Service Level Agreement (SLA) jobs with end time inaccuracies, and generating fix recommendations to enhance prediction accuracy by fixing data inaccuracy and insufficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing predictive solutions are implemented, then prediction capability is provided, but prediction accuracy is limited due to data gaps and inconsistencies
Solution Approach 1:
The system performs preliminary actions by proactively predicting batch process outcomes before execution using historical data and metadata. The predictive model analyzes past performance patterns to forecast future batch process results, enabling early identification of potential issues and accurate prediction of batch completions before they occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual batch process outcomes with predicted outcomes. This feedback loop allows the system to learn from discrepancies, refine prediction models, and improve accuracy over time. The feedback also triggers proactive notifications when deviations from expected behavior are detected.
2Measurement precision
If manual effort is increased to address data quality issues, then data accuracy improves, but operational efficiency decreases
Solution Approach 1:
The system enables self-service by automatically detecting data quality issues and generating proactive notifications without requiring manual intervention. The predictive model autonomously analyzes historical data and metadata to identify patterns indicating potential problems, and automatically alerts relevant personnel before issues impact batch operations, eliminating the need for manual data quality monitoring.
Solution Approach 2:
The system replaces manual mechanical processes with automated computational analysis. Instead of manually reviewing and cleaning data, the predictive model uses algorithms to automatically process historical data and metadata, identify patterns, and generate predictions. This substitution of automated intelligence for manual effort maintains data accuracy while significantly improving operational efficiency.
3Productivity
If batch process complexity increases with more jobs and interdependencies, then processing capability improves, but transparency and prediction reliability deteriorate
Solution Approach 1:
The system applies segmentation by breaking down complex batch processes into individual batch jobs and analyzing them separately using the predictive model. Each job's historical data and metadata are processed independently to generate specific predictions, which are then aggregated to provide overall batch process forecasts. This segmentation maintains transparency by allowing tracking of individual job predictions while handling complex interdependencies.
Solution Approach 2:
The predictive model serves multiple functions simultaneously: it predicts batch completion times, identifies potential data quality issues, generates proactive notifications, and analyzes various types of batch processes across different industries. This universal approach maintains transparency by providing a unified prediction framework that handles diverse batch processing scenarios with consistent methodology.
Data Source
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AI summary
Data inaccuracy and insufficiency are critical aspects to be analyzed to improve batch predictions, specifically in context of SLA jobs as they are foremost in affecting deliverables. Embodiments of the present disclosure provide a method and system for enhancing batch predictions by localizing jobs contributing to time deviation and generating fix recommendations by fixing data inaccuracy and insufficiency. The term fix recommendation refers to recommending a list of plausible fixes to identified causes that reduce batch prediction errors enhancing accuracy of predictions. The localization is performed by bottom-up traversing of a batch graph representing a batch process, if the batch process has a Service level Agreement (SLA) job, by narrowing down to the SLA job that has end time inaccuracies. The localization enables identifying the origin or real contributors and root cause analysis is performed for the localized jobs to generate effective fix recommendations by fixing data inaccuracy and insufficiency.