Machine-Learning Orchestration for Adaptive Investigative Data Routing
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Solution Overview
Problem
Existing predictive data analysis systems face inefficiencies in resource allocation and processing time due to limitations in automated decision-making for complex investigative processes.
Innovation Solution
A machine-learning based orchestration model is trained to optimize computing resources using a feedback loop, comprising multiple sub-models tailored for different portions of the investigative process, enabling intelligent routing and placement of data objects for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple predictive data analysis sub-routines are used to handle complex investigative processes, then the capability to process diverse data objects is improved, but the complexity of resource allocation and processing orchestration increases
Solution Approach 1:
An orchestration model is introduced as an intermediary component that manages the coordination between multiple predictive data analysis sub-routines. The orchestration model receives input data objects, determines the appropriate sub-routine to execute based on the object's characteristics and the investigative process requirements, and manages the overall processing flow. This intermediary layer simplifies the complexity of directly managing multiple sub-routines by providing a centralized decision-making mechanism that routes tasks appropriately without requiring complex direct coordination between all components.
2Productivity
If automated decision-making is implemented for resource allocation, then processing efficiency is improved, but the accuracy of routing decisions deteriorates due to lack of contextual understanding
Solution Approach 1:
The system implements a feedback mechanism where the orchestration model learns from the outcomes of previous routing decisions. By analyzing the results of executed sub-routines and the characteristics of processed data objects, the model refines its decision-making algorithm to improve future routing accuracy. This feedback loop enables the automated system to progressively enhance its contextual understanding and decision precision while maintaining high processing efficiency.
3Reliability
If computing resources are allocated to process all data objects thoroughly, then investigative outcomes are improved, but resource consumption and processing time increase
Solution Approach 1:
The system applies local quality by allocating computing resources selectively based on the specific characteristics and requirements of each data object. Rather than applying uniform thorough processing to all objects, the orchestration model identifies which data objects require intensive analysis and which can be processed with fewer resources. This differentiated approach ensures that investigative outcomes are maintained for critical cases while reducing unnecessary resource consumption and processing time for less critical data objects.
Data Source
AI summary
Various embodiments of the present invention disclose techniques for orchestrating a complex data processing scheme for an investigative process using a machine-learning based orchestration model that is trained to optimize the use of computing resources based at least in part on a feedback loop. An input data object associated with an investigative process can be selected for investigation; a predictive data analysis sub-routine for processing the input data object can be intelligently selected from a plurality of predictive data analysis sub-routines by the machine-learning based orchestration model; and a processing orchestration action can be initiated based at least in part on an investigative score output by the predictive data analysis sub-routine. The processing orchestration action can include closing the input data object, continuing to process the input data object with additional predictive data analysis sub-routines, or passing the input data object to a predictive entity for further processing.