Subject Data Platform With ML Standardization and Secure Workspaces
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Solution Overview
Problem
The complexity and fragmentation of subject-related data in fields like biotechnology and healthcare, combined with privacy concerns and varying data formats, hinder effective data development and accessibility for enterprises and individuals.
Innovation Solution
A computer system and method that standardizes and structures subject-related information from diverse sources into a uniform format using machine learning, enabling secure data development and user-controlled operations within a virtual environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If subject-related data is collected from diverse sources in various formats, then data quantity and information type are improved, but data complexity and processing difficulty increase
Solution Approach 1:
The system segments data processing into distinct functional modules: data receiving module for collection, data extraction and classification module for processing, data engineering module for standardization, and storing module for secure storage. Each module handles specific aspects of the data pipeline, making the complex processing task manageable and systematic.
Solution Approach 2:
The data extraction and classification module acts as an intermediary between diverse data sources and the uniform dataset requirement. It receives data in various formats from multiple sources and transforms them into standardized structures, mediating between heterogeneity and uniformity.
2Ease of operation
If data is standardized and structured into uniform format, then data accessibility and usability are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary standardization and structuring of data during the data engineering phase, before the data is needed for analysis. By pre-processing and uniforming the data structure in advance, the system eliminates the need for repeated processing when data is accessed, thereby reducing future processing time while maintaining high accessibility.
3Reliability
If secure data repositories are implemented with privacy regulations, then data security and compliance are improved, but data accessibility and sharing are restricted
Solution Approach 1:
The system creates and works with copies of data in standardized formats within secure virtual environments. Users can import and analyze data copies without accessing the original sensitive data sources directly, maintaining security while enabling accessibility for authorized operations.
4Extent of automation
If machine learning models are used for data standardization, then data processing automation and consistency are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The machine learning models perform self-service data standardization by automatically learning patterns and relationships from the input data. The models autonomously transform diverse data formats into uniform structures without requiring manual configuration for each data type, achieving high automation while managing complexity through intelligent self-adaptation.
Data Source
AI summary
A method comprises receiving at least one input data object containing subject-related information according to at least one of information types encoded in at least one of data formats; and processing the at least one input data object for standardizing the subject-related information. The method further includes subjecting the subject-related information to a first machine learning model for generating a uniform dataset containing the subject-related information in a uniform structured format; storing the uniform dataset in one or more secured data repositories connected to a network; and providing a secured virtual environment accessible to users connected to the network, the secured virtual environment enabling importation of datasets stored in the one or more secured data repositories and a use of imported datasets as part of one or more user-controlled subject-related data development operations for generating at least one workspace-developed data object.