Data Input Platform with Differential Graph Representations
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Solution Overview
Problem
Current Laboratory Information Management Systems (LIMS) and Manufacturing Execution Systems (MES) face challenges in flexibility, data entry efficiency, and visualization of complex workflows, particularly in research and development environments where workflows and data structures change rapidly, leading to increased costs and friction in capturing and structuring data.
Innovation Solution
A data input platform that removes workflow prescription constraints, allowing parallel instructions for data processing, prioritizing data input over validation, and representing records as graphs to emerge from user descriptions, enabling batch data entry, incomplete data handling, and flexible association creation, with the generation of summary block flow diagrams for improved visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined workflows with validation constraints are used to protect data integrity, then data reliability is improved, but data entry efficiency deteriorates due to increased friction and manual steps
Solution Approach 1:
The system dynamically adjusts validation constraints based on workflow context and data type. Rather than applying rigid pre-defined validation rules to all data entries, the system adapts validation requirements to match the specific workflow step and data being entered, reducing unnecessary friction while maintaining data integrity for critical fields
Solution Approach 2:
The system changes validation parameters dynamically based on the workflow state, data type, and user context. Validation strictness is adjusted as a variable parameter rather than a fixed constraint, allowing flexible data entry in early workflow stages while maintaining strict validation at critical decision points
2Productivity
If structured data formats are used for efficient storage and retrieval, then data storage efficiency is improved, but flexibility in handling custom and evolving data structures deteriorates
Solution Approach 1:
The system segments data storage into structured core fields and flexible extension fields. Critical data elements are stored in structured formats for efficient retrieval, while custom and evolving data attributes are stored in flexible key-value pairs or JSON fields, allowing both efficiency and adaptability
Solution Approach 2:
The data storage structure combines multiple data organization approaches into a composite model: relational structured data for core workflow elements, semi-structured documents for step-specific data, and flexible key-value stores for custom attributes. This composite approach leverages the strengths of each format
3Reliability
If manual workflow setup by privileged users is required, then workflow configuration control is improved, but ease of operation deteriorates due to increased complexity and time requirements
Solution Approach 1:
The system provides pre-configured workflow templates and building blocks that are prepared in advance by administrators. Users can assemble workflows from these pre-validated components without manual configuration of each parameter, reducing setup time while maintaining configuration control through template management
Solution Approach 2:
The system enables end users to perform workflow configuration and customization themselves through intuitive interfaces and drag-and-drop functionality. Users can modify workflows, add steps, and configure parameters without requiring privileged access or administrator intervention
4Manufacturing precision
If complete data validation is performed before data entry acceptance, then data quality is improved, but data entry speed deteriorates due to additional verification steps
Solution Approach 1:
The system performs partial validation during data entry, checking only critical fields and data types immediately to enable fast entry. Comprehensive validation is performed asynchronously or on-demand, providing data quality assurance without blocking the data entry flow and reducing perceived verification steps
Data Source
AI summary
A method of processing data comprises receiving, at a processor, first data comprising a plurality of values; receiving second data comprising one or more directives and association information; retrieving a first set of records from the first data using a first subset of the second data; generating a second set of records from the second data using a second subset of the second data; generating, based on a third subset of the second data, a plurality of records associations, the plurality of records associations including one or more of: first relationships between records of the first set of records, second relationships between records of the second set of records, and third relationships between records of the first set of records and the second set of records; storing the plurality of records associations in a structured format; and generating feedback data based on the plurality of records associations.