Background AI Analysis for Continuous Signal Detection in Software
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Solution Overview
Problem
Existing software applications lack continuous background data analysis capabilities using artificial intelligence, requiring manual intervention for signal hunting and repetitive analysis, leading to inefficiencies and unnecessary resource consumption.
Innovation Solution
Implement a data analysis activity (DAA) that defines queries and instructions for AI engines to run continuously in the background, notifying users only when significant signals are detected, allowing for flexible configuration and customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analysis and signal hunting are performed by data scientists, then analysis quality and insight accuracy are improved, but time consumption and resource costs increase
Solution Approach 1:
The system pre-configures multiple analysis definitions with different criteria, parameters, and AI models before execution. These pre-defined analysis templates enable automated background jobs to immediately begin analysis without requiring manual setup each time, thus reducing the time to deliver insights while maintaining consistent analysis quality through predefined accurate methodologies
Solution Approach 2:
The system enables self-service automated analysis where background jobs autonomously execute predefined analysis definitions, trigger notifications when signals are detected, and manage their own execution cycles without continuous human intervention. This automation eliminates the need for data scientists to manually repeat analysis tasks while maintaining analysis accuracy through consistent application of predefined criteria
2Loss of information
If repetitive manual analysis is performed, then valuable insights can be discovered, but resource consumption and operational costs increase
Solution Approach 1:
The system implements continuous background analysis jobs that automatically and continuously monitor data sources for signals matching predefined criteria. Unlike manual analysis that occurs periodically when resources are available, the automated background jobs run continuously without interruption, ensuring signals are detected as soon as they appear while optimizing resource usage through efficient automated execution
Solution Approach 2:
The system incorporates feedback mechanisms where analysis results trigger notifications to users when specific signals or anomalies are detected. This feedback loop allows the system to automatically adjust its operation - continuing analysis when signals are present and efficiently idle when data remains normal - thereby maintaining signal detection capability while reducing unnecessary resource consumption during periods without interesting patterns
3Productivity
If automated background analysis is implemented, then time to deliver insights is reduced, but system complexity and configuration requirements increase
Solution Approach 1:
The system employs universal analysis definitions that can be configured once and then reused across multiple background jobs with different parameters. These multi-functional analysis templates can serve various purposes (different data sources, time periods, or business questions) without requiring separate custom configurations for each use case, thereby reducing overall system complexity while enabling rapid deployment of multiple automated analysis instances
Solution Approach 2:
The system allows flexible parameterization of analysis definitions where core analysis logic remains fixed but executable parameters (such as data sources, time ranges, threshold values, or target audiences) can be easily modified. This parameter-driven approach enables quick adaptation and configuration of automated analysis jobs without requiring complex system changes, thus maintaining high productivity while managing configuration complexity through standardized parameter interfaces
Data Source
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AI summary
Using a data analysis activity (DAA) definition, a DAA associated with a software application is triggered. An instance selector query is executed to generate a set of instance values as input for a data query. A data query to generate a data set is executed using instance values of the set of instance values. Using the data set, an instruction for an artificial intelligence (AI) engine is computed. A result based on the instruction for an AI engine is received from the AI engine. The result based on the instruction for an AI engine is stored into an AI Result History Store. Prior results from earlier DAA executions is read from the AI Result History Store. A notification to a defined target audience is sent using the software application.