Distributed Random Forest Processing for Graceful Degradation
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Solution Overview
Problem
Existing machine learning algorithms, such as random forest models, are limited by single-processor implementations, which can lead to disruptions and inaccurate classification when operating in fragile or chaotic environments, and are invariant under scaling transformations.
Innovation Solution
A distributed implementation of random forest modeling across multiple geographically dispersed computing nodes, where each node processes a distinct independent variable of the model, allowing for robust operation even if some nodes become unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single processor is used to implement machine learning algorithms, then the device complexity is reduced, but the reliability deteriorates because the entire process is disrupted when the processor becomes unavailable
Solution Approach 1:
The patent divides the random forest predictive model into multiple independent decision tree processors, where each processor handles a specific decision tree. This segmentation allows the system to maintain functionality even when individual processors fail, as other processors continue to operate independently, thus improving reliability without significantly increasing overall system complexity.
2Device complexity
If a single processor is used to implement random forest models, then the device complexity is reduced, but the productivity deteriorates due to limitations in processing capacity
Solution Approach 1:
The patent combines multiple independent processors into a unified random forest system, where each processor contributes a decision tree to the overall predictive model. This merging approach increases processing capacity and productivity by utilizing parallel computation across multiple processors while maintaining manageable complexity through modular architecture and coordinated output aggregation.
3Reliability
If random forest models are implemented with distributed processing, then the reliability is improved through graceful degradation, but the device complexity increases due to multiple geographically dispersed computing nodes
Solution Approach 1:
The patent segments the random forest model into distributed decision tree processors located at different geographic nodes in a network. Each node independently processes its assigned decision tree, enabling the system to maintain predictive capabilities even when some nodes become unavailable. This segmentation provides graceful degradation while managing complexity through modular, independent processing units.
4Ease of operation
If random forest models are performed by a single processor, then the ease of operation is maintained, but the measurement precision deteriorates leading to inaccurate classification
Solution Approach 1:
The patent merges multiple processor outputs into a comprehensive predictive model, where each processor contributes decision tree results that are aggregated to form the final random forest prediction. This merging approach improves classification accuracy by incorporating diverse computational perspectives while maintaining ease of operation through standardized interfaces and unified model output.
Data Source
AI summary
A network includes a server and a plurality of geographically dispersed computing nodes connected to the server. A detector is coupled to one or more of the computing nodes. A plurality of processors are included. A first processor is in a first computing node. A second processor is in a second computing node. The server is configured to generate a predictive model of an event detected by the detector. The server is configured to distribute predictive modelling computations of the predictive model, in parts across the geographically dispersed computing nodes. The first processor in the first computing node performs at least a first part of the predictive modelling computations. The second processor in the second computing node performs at least a second part of the predictive modelling computations.