Analytics Validation Engine Subset Processing
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Solution Overview
Problem
Existing analytics systems in cloud computing require full dataset processing for validation, which is time-consuming and costly, especially for organizations adopting new systems or processing new data types.
Innovation Solution
A validation engine processes a subset of the dataset using a test script and compares it with the analytics system's output subset, allowing for partial validation before the entire dataset is processed, utilizing asynchronous callbacks and RESTful APIs for efficient data comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full dataset processing is used for validation, then validation completeness is improved, but validation time and cost increase
Solution Approach 1:
The patent divides the validation process into segments by processing and validating a subset of the dataset rather than the entire dataset. The validation engine receives a subset of the input data, processes it through the analytics system, and compares the results against expected outcomes. This segmentation allows validation to be performed quickly on a representative portion of the data, sacrificing some completeness for significant time savings.
Solution Approach 2:
The patent applies partial action by performing validation on only a portion (subset) of the complete dataset rather than processing the entire dataset. This approach validates the analytics system's correctness on representative data without incurring the full time and computational cost of processing all data, thus resolving the contradiction between validation completeness and validation time.
2Measurement precision
If full dataset processing is used for validation, then validation accuracy is improved, but computational cost increases
Solution Approach 1:
The validation process is segmented to operate on a subset of the dataset rather than the complete dataset. This reduces the computational resources required for validation while maintaining sufficient accuracy through careful selection of the subset. The segmentation principle allows the system to validate analytics correctness without incurring prohibitive computational costs.
Solution Approach 2:
The patent extracts a representative subset from the complete dataset for validation purposes. By taking out only the necessary portion of data required to validate the analytics system's correctness, the system achieves adequate validation accuracy while significantly reducing computational cost and energy consumption associated with processing the entire dataset.
3Productivity
If subset processing is used for validation, then validation speed is improved, but validation thoroughness decreases
Solution Approach 1:
The patent implements partial action by validating only a subset of the dataset rather than the complete dataset. This approach prioritizes validation speed over complete thoroughness, accepting that not all data points will be validated. The partial validation on a representative subset provides timely feedback on analytics system correctness without claiming complete coverage.
Solution Approach 2:
The validation process is segmented to focus on a specific subset of data rather than attempting to validate everything. This segmentation enables faster validation execution by limiting the scope to essential test cases, while the thoroughness is maintained within the context of the selected subset through careful test case selection and comprehensive comparison logic.
Data Source
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AI summary
Methods,systems,and apparatus,including computer programs encoded on computer storage media,for validating analytics results.One of the methods includes processing a subset of a dataset and polling an analytics system for a corresponding output subset and comparing the two subsets to validate the analytics system.