ML Pipeline Operational Semantics for Distributed Lineage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions struggle to efficiently manage complex multi-step machine learning pipelines, particularly in distributed environments, and lack effective mechanisms for lineage tracking and dynamic control of hardware devices.
Innovation Solution
A computer system and method that represents a machine learning pipeline using a directed acyclic graph (DAG) and data flow graph (DFG), annotating nodes with operational semantics and output object references to track lineage and control hardware devices dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning pipelines are represented without operational semantics annotations, then the pipeline structure is simple, but lineage tracking and device control capabilities are insufficient
Solution Approach 1:
The patent applies preliminary action by annotating pipeline nodes with operational semantics and object references before pipeline execution. This pre-processing step embeds metadata including input/output object references, operational semantics, and provenance information into the DFG structure, enabling subsequent lineage tracking and device control without requiring complex post-processing mechanisms
Solution Approach 2:
The patent introduces an intermediary layer of operational semantics annotations that mediate between the pipeline structure and lineage tracking requirements. These annotations serve as intermediaries that connect pipeline nodes to object provenance information, enabling traceability without directly modifying the core pipeline logic or data flow
2Loss of information
If distributed lineage tracking is implemented without selective annotation, then comprehensive tracking is achieved, but processing overhead increases significantly
Solution Approach 1:
The patent applies local quality by implementing selective annotation where only specific pipeline nodes that require lineage tracking are annotated with operational semantics and object references. This localized approach ensures that provenance information is captured for critical nodes while avoiding unnecessary annotation overhead for nodes that do not require tracking, thus maintaining processing efficiency
Solution Approach 2:
The patent uses partial action by annotating only the necessary portion of pipeline nodes rather than all nodes. The selective annotation strategy applies operational semantics and object references only to nodes where lineage information is needed, achieving sufficient provenance tracking without the excessive overhead of universal annotation
3Adaptability or versatility
If dynamic device control is implemented without object references, then system simplicity is maintained, but hardware device control capability is limited
Solution Approach 1:
The patent applies universality by designing operational semantics annotations that serve multiple functions simultaneously. The same annotation structure provides both lineage tracking information and device control capabilities, allowing the pipeline representation to be used for both provenance tracking and hardware control without requiring separate mechanisms
Solution Approach 2:
The patent uses copying by creating reference copies of object identifiers within the pipeline annotations. Object references are copied into the operational semantics annotations, enabling the pipeline to reference external hardware devices without direct coupling, thus maintaining system simplicity while enabling device control capability
4Reliability
If remediation actions are applied without provenance identification, then error handling is basic, but targeted remediation at specific pipeline locations is not possible
Solution Approach 1:
The patent applies feedback by using captured lineage information to guide remediation actions. When errors occur, the system traces back through the annotated operational semantics and object references to identify the provenance of affected objects, then applies remediation actions at the specific pipeline nodes that caused the issue, creating a feedback loop from error detection to targeted correction
Data Source
AI summary
A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is represented in a data flow graph (DFG). The represented pipeline is subject to annotations, with the annotations including pipeline nodes and object references. The pre-processed pipeline is subject to execution or processing with the annotated object references capturing object lineage. Output from the executed pipeline is constructed and a corresponding control signal is formatted to dynamically and selectively control an operatively coupled physical hardware device or software.