Scoped Parameter Contexts for Dependency Graph Execution
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Solution Overview
Problem
Existing job control systems face challenges in managing parameter dependencies and conflicts across tasks in a dependency graph, leading to ambiguities and uncertainties in parameter values during task execution.
Innovation Solution
The method involves maintaining multiple parameter contexts based on the dependency graph, allowing each task to bind values to parameters within its scope, and using explicit parameter passing to check for conflicts and ambiguities, ensuring consistent parameter values across repeated executions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single global parameter context is used, then parameter management is simple, but name conflicts and ambiguities occur when peer tasks access shared parameters
Solution Approach 1:
The patent divides the single global parameter context into multiple parameter contexts, each associated with a specific task or task group in the dependency graph. This segmentation allows parameters to be scoped to specific tasks, preventing name conflicts when peer tasks access shared parameters, while maintaining simple access patterns within each context.
Solution Approach 2:
The patent introduces a new dimension to parameter management by associating parameter contexts with tasks in the dependency graph rather than using a flat global scope. This dimensional approach organizes parameters along the task execution hierarchy, enabling clear scoping rules that resolve ambiguities while preserving execution efficiency.
2Reliability
If parameters are passed explicitly between tasks, then conflicts and ambiguities can be checked, but the system complexity increases
Solution Approach 1:
The patent introduces parameter contexts as intermediary structures that mediate between tasks. Instead of direct complex parameter passing, tasks interact through these contextual intermediaries that automatically manage parameter binding, scoping, and conflict detection, simplifying the overall mechanism while ensuring reliability.
Solution Approach 2:
The system incorporates feedback mechanisms that automatically detect parameter conflicts and ambiguities by analyzing the dependency graph and parameter bindings. This feedback allows the system to identify issues before execution and provide guidance for resolving conflicts, reducing the need for manual complexity management.
3Reliability
If multiple parameter contexts are maintained according to dependency graph, then name conflicts are reduced, but the overhead for maintaining contexts increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing parameter contexts and their scopes based on the dependency graph before task execution begins. This preliminary setup eliminates the need for runtime context creation and reduces overhead during actual task execution, improving productivity while maintaining reliable parameter isolation.
Solution Approach 2:
The system dynamically adjusts parameter context usage based on the actual task execution path. When tasks execute in parallel, the system efficiently manages multiple contexts without significant overhead. When tasks execute sequentially, the system reuses contexts to minimize overhead, thereby optimizing productivity while maintaining scope isolation.
4Adaptability or versatility
If peer tasks can share parameters, then adaptability improves, but ambiguities and conflicts increase without proper scoping rules
Solution Approach 1:
The patent creates a universal parameter context framework that serves multiple functions: it enables parameter sharing between peer tasks, provides clear scoping rules, detects conflicts, and maintains adaptability. This multi-functional approach allows the system to handle various parameter sharing scenarios without requiring separate mechanisms for each case.
Data Source
AI summary
A number of tasks are defined according to a dependency graph. Multiple parameter contexts are maintained, each associated with a different scope of the tasks. A parameter used in a first of the tasks is bound to a value. This binding includes identifying a first of the contexts according to the dependency graph and retrieving the value for the parameter from the identified context.