Automated Spreadsheet Input Validation via Allowed Directives
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Solution Overview
Problem
Desktop and web-based spreadsheets require manual interaction for managing input data, leading to errors and inefficiencies, especially with complex data sets, and lack advanced features for automated processing and validation.
Innovation Solution
A data processing platform analyzes source spreadsheets to determine input data criteria, generates sample data, and creates allowed input directives for validating compatibility, enabling automated testing and debugging to improve spreadsheet reliability and error handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual interaction is used to manage input data in spreadsheets, then ease of operation is maintained, but reliability deteriorates due to human errors and inefficiencies
Solution Approach 1:
The system performs preliminary validation of input data against predefined criteria before processing. It automatically generates and applies allowed input directives that specify acceptable data formats, types, and ranges, preventing errors before they occur rather than detecting them after processing
Solution Approach 2:
The spreadsheet system automatically validates its own input data without requiring manual intervention. The built-in validation mechanisms self-check data conformity to criteria, automatically identify and report issues, and enforce data quality standards independently of user actions
2Reliability
If manual validation of input data is performed, then reliability improves, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The system replaces manual mechanical validation processes with automated computational validation. Algorithms automatically check input data against predefined criteria, eliminating the need for human review of each data point while significantly improving both reliability and processing speed
Solution Approach 2:
Validation occurs continuously as data is entered or imported, rather than requiring separate batch validation steps. The system maintains constant monitoring and verification of data quality throughout the entire processing workflow, eliminating idle time between data collection and validation
3Reliability
If simple spreadsheets are used for critical calculations, then ease of operation is maintained, but reliability deteriorates due to limited error handling ability
Solution Approach 1:
The system segments validation functionality into distinct, modular components. Separate validation modules handle different aspects of data verification (format validation, type checking, range validation, cross-field consistency), making the complex error handling system manageable and maintainable while improving reliability
Solution Approach 2:
The system implements comprehensive error handling mechanisms that prevent critical failures before they occur. By validating input data against strict criteria and providing preemptive error detection, the system cushions against potential calculation failures and data corruption in critical financial models
Data Source
AI summary
The present teachings generally include a data processing platform (e.g., a platform hosted by a remote computing resource) that analyzes and compiles information contained in a source spreadsheet, e.g., to determine input data criteria for compatibility with functionality of the spreadsheet. The data processing platform may generate sample input data useful for testing the input data criteria, and create an allowed input directive for the spreadsheet based upon feedback using the input data criteria. The data processing platform may also, or instead, automatically generate an allowed input directive that defines input data criteria, and validate the allowed input directive using automatically generated sample input data. Techniques disclosed may also or instead include iterative testing and debugging processes for improving the reliability of spreadsheets for data processing.