Automated Rule Generation via Data Trend Delta Analysis
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Solution Overview
Problem
Existing trend analysis methods rely on manually defined rules that are generic and not adaptable to specific environments, leading to inaccurate monitoring, high costs, and increased efforts due to the need for frequent updates and potential errors in rule generation and validation.
Innovation Solution
An automated system that generates and updates rules based on data trend analysis by computing delta values and identifying relationships between data streams, providing notifications and suggestions for rule violations, and allowing for dynamic updates based on user responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual rule definition is used for trend analysis, then rule generation depends on individual skill sets, but this leads to reduced accuracy and increased effort for rule generation and validation
Solution Approach 1:
The system performs self-service by automatically generating rules through trend analysis of historical data without requiring manual intervention. The automated rule generation system analyzes data patterns, computes delta values, and formulates rules independently, eliminating dependence on individual skill sets while reducing validation effort through systematic verification processes.
Solution Approach 2:
The patent replaces the mechanical process of manual rule definition with an automated computational system. Instead of relying on human experts to manually create and validate rules, the system uses algorithmic trend analysis, data processing, and automated rule formulation mechanisms that objectively generate accurate rules without human intervention.
2Adaptability or versatility
If generic rules are used for monitoring, then they can be applied broadly, but they become inaccurate for specific environments and require frequent updates
Solution Approach 1:
The system applies local quality by generating environment-specific rules through trend analysis of local historical data. Instead of using uniform generic rules across all environments, the system analyzes data patterns specific to each environment and formulates tailored rules that accurately reflect local characteristics, thereby maintaining high reliability without requiring frequent updates.
Solution Approach 2:
The patent implements dynamics by enabling rules to adapt automatically to changing environmental conditions through continuous trend analysis. The system monitors data patterns over time and dynamically adjusts rules to reflect current environment-specific characteristics, ensuring ongoing accuracy and reliability without manual intervention for updates.
3Adaptability or versatility
If manual rule updates are performed frequently, then rules can adapt to changes, but this increases costs and efforts significantly
Solution Approach 1:
The system ensures continuity of useful action by performing automated trend analysis and rule generation continuously without interruption. The automated system continuously monitors data patterns, detects changes in trends, and updates rules automatically, eliminating the need for frequent manual updates while maintaining adaptability to environmental changes, thereby saving time and reducing costs.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors data trends and automatically adjusts rules based on detected patterns and deviations. This closed-loop feedback system enables the rules to adapt to changes automatically, eliminating the need for manual intervention and reducing both time and costs associated with frequent rule updates while maintaining high adaptability.
Data Source
AI summary
The present subject matter relates to monitoring of an environment using data trend analysis. The method comprises obtaining at least one data trend pertaining to at least one data stream for a pre-defined period of time. The at least one data trend is indicative of a pattern followed by the at least one data stream. Further, at least one delta value pertaining to the at least one data stream may be computed, the at least one delta value being indicative of a deviation in the at least one data stream with respect to the at least one data trend at a specific time instance. Furthermore, at least one relationship between a plurality of data streams is identified based on the at least one data trend and identity metadata associated with each data stream. Based on the at least one delta value and the at least one relationship, at least one rule is generated.