Validation Engine for Cloud Software Input Data
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Solution Overview
Problem
Cloud-based software applications face challenges in validating complex input data, leading to computational resource wastage and difficult troubleshooting due to inconsistent or invalid data, especially in computationally intensive operations.
Innovation Solution
A validation engine determines validation classes for input data subsets, performs specific validation operations, and generates reports to indicate errors, allowing for separate validation processes to be executed locally or on cloud-based assets, thereby preventing invalid data from being processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive validation procedures are implemented for all data subsets, then data consistency and reliability are improved, but computational resources and implementation complexity increase significantly
Solution Approach 1:
The validation process is divided into separate validation procedures for each data subset (geometrical definition, initial conditions, boundary conditions, material properties, etc.). Each validation class handles specific validation rules for its corresponding data type, making the overall complex validation process manageable and implementable through modular, specialized components rather than a single monolithic validation system.
Solution Approach 2:
Each validation class is designed with specialized validation rules tailored to the specific characteristics and requirements of its corresponding data subset. For example, geometrical validation checks mesh quality and boundary definitions, while material property validation checks physical property ranges and consistency, ensuring that each data type receives appropriate validation without unnecessary complexity from generic validation approaches.
2Reliability
If extensive validation operations are performed on complex input data, then data errors are detected, but computational resources are consumed before processing begins
Solution Approach 1:
Validation operations are performed on input data before the main computational processing begins. By executing validation procedures to detect errors in geometrical definitions, initial conditions, boundary conditions, and material properties upfront, the system prevents wasted computational resources on processing invalid data that would ultimately produce meaningless results or cause processing failures.
3Reliability
If validation errors are detected in complex input data, then data quality is maintained, but troubleshooting and correction become difficult due to data complexity
Solution Approach 1:
Validation errors are identified and reported by specific validation classes corresponding to particular data subsets (e.g., geometrical validation errors, boundary condition errors, material property errors). This segmentation allows users to quickly locate and understand which specific part of the complex input data contains errors, making troubleshooting and correction significantly easier compared to undifferentiated error reporting from a monolithic validation system.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for validating a set of input data used by a software application, the method comprising: determining a first validation class for a first portion of the set of input data; determining a first validation operation to be performed on the first portion of the set of input data based on the first validation class; causing the first validation operation to be performed on the first portion of the set of input data; determining that the first validation operation is unsuccessful; and generating a validation report indicating that the set of input data includes an error.