Machine Learning Standardization in Project Data Storage
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Solution Overview
Problem
Conventional project data storage techniques struggle to accurately extract, classify, and store data from diverse entities using non-standardized formats, leading to misclassification and erroneous project metrics due to the highly variable formats utilized by these entities.
Innovation Solution
A system utilizing a trained machine learning model to extract, analyze, and convert non-standardized data to a standardized format, store it in a project database, and generate indications of the data and predicted impacts for display, while also employing a nesting data module to optimize storage capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to store data from diverse entities, then data storage capacity is maintained, but data classification accuracy deteriorates due to highly variable non-standardized formats
Solution Approach 1:
The patent introduces an intermediary processing layer between data receipt and storage that standardizes non-standardized formats. This intermediary system extracts data from various formats, classifies it using trained machine learning models, and converts it to standardized formats before storage, thereby maintaining classification accuracy despite format variability.
Solution Approach 2:
The system changes the parameter of data format from non-standardized to standardized through automated processing. Trained machine learning models detect and classify data in non-standardized formats, then transform them into standardized formats with consistent parameters, enabling accurate classification while accommodating diverse input formats.
2Measurement precision
If conventional data extraction methods are used, then processing speed is maintained, but data classification accuracy deteriorates leading to erroneous metrics
Solution Approach 1:
The system performs preliminary classification and standardization of data before storage and analysis. Trained machine learning models pre-process incoming data by detecting its format, classifying it accurately, and converting it to standardized formats, thereby preventing classification errors before they propagate to metric calculations while maintaining processing efficiency through automation.
3Measurement precision
If manual data correction processes are implemented, then data accuracy is improved, but time and resource consumption increases
Solution Approach 1:
The system implements self-service data classification and correction through trained machine learning models that automatically detect, classify, and standardize data formats without human intervention. The models learn from training data to accurately classify non-standardized formats and automatically correct classification errors, eliminating the need for manual correction processes and reducing time and resource consumption.
Data Source
AI summary
Techniques for optimizing project data storage are disclosed. An example system includes processors and memories communicatively coupled with the processors storing a trained machine learning (ML) model, a data inbox, a project database associated with a project, and instructions that cause the processors to: receive, at the data inbox, an input including data corresponding to the project, wherein the data is formatted in accordance with a non-standardized format; execute the trained ML model to: extract the data from the input, and analyze the data to output (i) a predicted classification and (ii) a predicted impact associated with the project; convert the data to a standardized format based on the predicted classification; store (i) the data and (ii) the predicted impact in the project database; and generate an indication of the data and the predicted impact for display to a user as part of the data inbox.