ML Workflow Graph Embedding for Accurate Flow Clustering
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Solution Overview
Problem
Current machine learning workflow management systems face challenges in accurately clustering complex workflows due to cognitive overload and lack of contextual information, particularly in parallel environments, where properties such as operation type, data processing, and infrastructure are crucial for proper segregation.
Innovation Solution
A system and method that embeds workflow graphs in a coordinate system, incorporating operation and data properties, and uses adaptive resonance clustering to identify and label similar workflows, providing a graphical user interface for review and editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine learning workflows are clustered based solely on graph properties, then the clustering process is simple, but the accuracy of workflow segregation is insufficient
Solution Approach 1:
The patent transitions from clustering workflows based solely on graph properties (2D spatial representation) to incorporating temporal properties and sequential information (adding a time dimension). This multi-dimensional approach embeds workflows in a coordinate system that considers both structural graph properties and temporal execution characteristics, thereby improving segregation accuracy without significantly complicating the clustering process.
Solution Approach 2:
The patent changes the parameters used for clustering from purely structural graph properties to a combination of structural properties and temporal properties. By introducing new parameters such as execution timing, sequential dependencies, and temporal patterns, the clustering algorithm achieves more accurate workflow segregation while maintaining computational feasibility.
2Measurement precision
If contextual information is added to improve workflow matching accuracy, then the precision of workflow automation increases, but the cognitive overload increases
Solution Approach 1:
The patent enables workflows to self-describe their temporal properties and sequential information through automatic property extraction and embedding. The system autonomously captures execution timing, dependency relationships, and temporal patterns without requiring manual annotation, thereby improving matching accuracy while avoiding the cognitive overload that would result from manual contextual information specification.
Solution Approach 2:
The patent replaces manual contextual information specification (mechanical process) with automated machine learning-based property extraction and embedding. The system uses algorithms to automatically identify and encode temporal properties, sequential dependencies, and execution patterns, substituting human cognitive effort with computational processes that scale without increasing cognitive load.
3Productivity
If traditional clustering methods are used without considering sequential dependencies, then the processing speed is fast, but the accuracy of time-dependent flow segregation is poor
Solution Approach 1:
The patent performs preliminary embedding of temporal properties and sequential information into the workflow representation before the clustering process. By pre-processing and encoding temporal characteristics such as execution timing and dependency relationships into the workflow models, the system enables fast clustering operations that automatically capture time-dependent patterns without requiring complex real-time analysis during the clustering phase.
Data Source
AI summary
A system and method for clustering machine learning workflows according to inclusion/exclusion criteria. The clustering is based on a plurality of information obtained from operators on the workflow, the position on the workflow of each operator and the data each operator is working on. The position of each operator on the workflow is obtained from its graph-based representation embedded on a coordinate system.