Machine Learning Model for Data Anomaly Remediation

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Solution Overview

Problem

Conventional database access applications lack the functionality to efficiently identify and remediate anomalies in data objects, making it time-consuming and difficult to address inconsistencies and issues in large datasets.

Innovation Solution

A machine learning model is trained using datasets with identified issues and remediation actions, allowing it to suggest updates and generate recommendations for anomalies in target datasets, displayed through a graphical user interface for user interaction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If conventional database access applications are used to identify and remediate anomalies in data objects, then the system maintains simplicity and ease of operation, but the time required to identify and address inconsistencies in large datasets increases significantly

Engineering Contradiction:

Improvetime required to identify and remediate anomaliesVSAvoidsystem complexity for anomaly detection and remediation

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-training machine learning models on historical data objects and their associated issues and remediation actions. This pre-processing enables the system to quickly suggest updates and generate remediation recommendations when anomalies are detected in target datasets, rather than requiring time-consuming manual analysis of each anomaly.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces machine learning models as intermediary components between the data objects and the user. These models act as mediators that automatically analyze data objects, identify issues, and generate remediation suggestions, reducing the time burden on users while managing system complexity through automated intelligence.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If manual methods are used to identify and remediate issues in large datasets, then the system maintains simplicity, but productivity and the speed of data remediation decrease

Engineering Contradiction:

Improvespeed of data remediationVSAvoidcomplexity of automated anomaly detection system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements self-service by enabling automated anomaly detection and remediation suggestion generation through machine learning models. The models independently analyze data objects, identify issues, and generate remediation recommendations without requiring manual intervention for each anomaly, thereby significantly improving productivity while managing complexity through automation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes of anomaly identification and remediation with automated machine learning-based systems. The machine learning models substitute for human analysts by automatically processing large datasets, identifying patterns and issues, and generating remediation suggestions, thus dramatically increasing remediation speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If conventional database systems are used without machine learning guidance, then the system maintains ease of operation, but the precision and accuracy of anomaly identification and remediation recommendations decrease

Engineering Contradiction:

Improveaccuracy of anomaly identificationVSAvoidcomplexity of machine learning model integration

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary training of machine learning models on historical data objects with known issues and remediation actions. This pre-processing phase enables the models to learn patterns and relationships in the data, improving the precision and accuracy of anomaly identification and remediation recommendations when deployed on target datasets.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where machine learning models are trained on historical data objects and their associated issues and remediation actions. This feedback loop allows the models to continuously improve their accuracy in identifying anomalies and generating effective remediation recommendations based on learned patterns from past data.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20230351176A1Machine-learning-guided issue resolution in data objects

Publication Date: 2023.11.02 ORACLE INT CORP

AI summary

Techniques for using a machine learning model to recommend remediation actions for issues identified in data objects are disclosed. A system applies a machine learning model to data representing one or more data objects to generate recommendations for remediating issues in the one or more data objects. The machine learning model is trained on training datasets of historical data object records. The training dataset identifies issues arising from the modifications and remediation actions addressing the issues. The system trains the machine learning model to learn correlations between identified issues and recommended remediation actions. The trained machine learning model recommends remediation actions for particular sets of data object data. The system presents the recommendations, together with a display of the data object, in a graphical user interface.