Machine Learning Error Prediction for Data Reporting Systems
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Solution Overview
Problem
Existing data reporting systems face issues with SQL query failures, leading to incomplete data retrieval and long wait times for users, who are not provided with error causes, prompting a trial-and-error approach to generate error-free reports, especially when selecting multiple parameters.
Innovation Solution
A system using incremental backpropagation to train machine learning models that predict error rates for data reports, allowing users to determine potential errors before execution and identify their causes, thereby reducing wait times and providing insights for error resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the user selects a large amount of parameters to generate a data report, then the comprehensiveness of the report is improved, but the time required for generation increases significantly (up to one hour or longer)
Solution Approach 1:
The system performs preliminary error rate prediction using a trained machine learning model before actually generating the data report. This allows the system to assess potential errors and provide early warnings, enabling users to adjust their parameter selections or expectations before committing significant computational resources and time to report generation.
Solution Approach 2:
The system implements a feedback mechanism where the machine learning model analyzes historical error data and provides real-time error rate predictions based on the user's selected parameters. This feedback loop allows users to understand the likelihood of errors before report generation, enabling them to make informed decisions about their parameter selections and potentially avoid problematic configurations.
2Speed
If the SQL query execution is performed without prior error checking, then the speed of report generation is improved, but the reliability of data retrieval deteriorates due to query failures
Solution Approach 1:
The machine learning model performs preliminary analysis of the intended SQL query parameters before actual query execution. By predicting error rates in advance, the system can identify potentially problematic queries without actually executing them, thus maintaining fast generation speeds while preventing unreliable data retrieval through early error detection.
Solution Approach 2:
The machine learning model acts as an intermediary layer between the user's parameter selections and the actual SQL query execution. This intermediary assesses the likelihood of errors before queries are run, providing a safety mechanism that maintains both speed (by not blocking all queries) and reliability (by identifying problematic queries before execution).
3Device complexity
If the system provides no error information to the user, then the system complexity is reduced, but the ease of error resolution deteriorates as users must engage in trial-and-error processes
Solution Approach 1:
The system provides targeted feedback to users by delivering specific error rate predictions and potential error causes based on their parameter selections. This feedback mechanism gives users actionable information about what might go wrong without requiring the system to implement complex real-time monitoring and debugging infrastructure, thus maintaining relatively simple system architecture while significantly improving error resolution ease.
Solution Approach 2:
The machine learning model enables users to self-diagnose potential errors by providing them with error rate predictions and cause indications before report generation. Users can independently assess their parameter selections and make adjustments without requiring system administrator intervention or complex debugging tools, thereby improving ease of operation without proportionally increasing system complexity.
Data Source
AI summary
Provided are systems for predicting an error rate and identifying the source of the error for generating a data report prior to generating the data report in a data reporting system that includes a processor to receive a dataset of a plurality of data instances, train a first prediction model based on data instances corresponding to a first category of parameters, train a second prediction model based on data instances corresponding to a second category of parameters, train a third prediction model based on data instances corresponding to a third category of parameters, train an ensemble machine learning model based on an output of the first prediction model, an output of the second prediction model, and an output of the third prediction model to provide a trained ensemble machine learning model, and generate a runtime output of the trained ensemble machine learning model based on a runtime input.