Data Model Detection via Paired Subgroup Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies struggle to detect changes or anomalies in datasets generated by black-box processes, where the transformation from input data to output data is hidden and unknown.
Innovation Solution
A computer system is provided that splits target datasets into paired subgroups and applies various detectors, such as pattern discovery tools, to detect changes, anomalies, and inconsistencies. Metrics generated by these detectors are then evaluated to assess the reliability of the data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the processing transformation from input data to output data is hidden or kept secret (black box operation), then the service can protect its proprietary algorithms and processing methods, but consumers cannot detect changes or anomalies in the processing
Solution Approach 1:
The patent introduces an intermediary detection system that operates between the black-box processing service and the consumers. This intermediary system receives both input and output data, applies pattern discovery algorithms, and generates detection metrics without requiring access to the internal processing logic. The intermediary acts as a mediator that can assess processing changes while respecting the black-box nature of the service.
Solution Approach 2:
The patent replaces the need for direct observation or access to processing mechanisms with a computational detection system. Instead of mechanically or directly examining the black-box processing, the system uses statistical pattern discovery and metric generation to infer processing changes from input-output data pairs.
2Measurement precision
If pattern discovery detectors are applied to detect changes and anomalies in datasets, then the detection capability and reliability are improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the detection process into distinct functional components: data pairing (grouping input-output pairs), pattern discovery (applying detection algorithms), metric generation (computing detection metrics), and evaluation (assessing results). This segmentation allows each component to be optimized independently and facilitates modular implementation, reducing overall system complexity while maintaining detection accuracy.
3Reliability
If multiple detectors are applied collectively to assess datasets, then the reliability of change detection is improved, but the computational resources and processing time required increase
Solution Approach 1:
The patent merges multiple detection functions into a unified evaluation system. Instead of running separate independent detection processes, the system combines pattern discovery, metric generation, and evaluation into an integrated workflow that processes datasets collectively. This merging reduces redundant computations and optimizes resource utilization while maintaining the reliability benefits of multiple detection approaches.
Data Source
AI summary
A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.