Workflow Graph Translation for Genomics Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computational workflow systems face challenges in scalability, reproducibility, and efficiency due to their reliance on specific syntax and the need for extensive user annotation, leading to increased processing time and costs, particularly in handling complex genomics data analyses.
Innovation Solution
The system translates computational workflows into a graph format, allowing jobs to proceed based on intermediate results availability, enabling efficient execution without extensive user annotation, and supporting dynamic expressions and parallelization, thus optimizing workflow execution across diverse environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational workflows use specific syntax and require extensive user annotation to ensure reproducibility, then workflow reliability is improved, but processing time and device complexity increase
Solution Approach 1:
The patent creates a standardized graph representation (copy) of computational workflows that captures the essential execution logic without requiring detailed syntax annotations. This graph model serves as a universal representation that can be executed across different environments, providing reproducibility while reducing the time needed for workflow specification and execution.
Solution Approach 2:
The patent transforms workflow descriptions from syntax-heavy formats into a parameter-based graph structure where nodes represent computational steps and edges represent data dependencies. This parameterization approach maintains reproducibility by preserving execution logic while reducing processing overhead through standardized representation.
2Productivity
If computational workflows are translated into graph format to enable parallelization and dynamic execution, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments computational workflows into discrete graph nodes representing individual computational steps, with edges representing data dependencies. This segmentation enables independent execution of nodes that have no dependencies, facilitating parallelization and improving productivity while maintaining manageable system complexity through modular structure.
Solution Approach 2:
The patent implements dynamic graph evaluation where the execution system can determine at runtime which nodes are ready for execution based on dependency satisfaction. This dynamic approach allows flexible parallelization and resource allocation, improving productivity without requiring complex static scheduling, thus balancing execution efficiency with system complexity.
3Reliability
If computational workflows require extensive user annotation and specific syntax, then workflow reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent creates a universal graph representation that can execute the same computational workflow across different environments and platforms. This standardized format serves multiple functions: it preserves reproducibility through structured dependency tracking while simultaneously improving portability by eliminating environment-specific syntax requirements, thus enhancing ease of operation.
4Productivity
If computational workflows are optimized for complex genomics data analysis, then productivity is improved, but loss of time increases due to extensive annotation requirements
Solution Approach 1:
The patent performs preliminary transformation of workflow descriptions into graph format before execution, capturing all necessary dependency information in advance. This preliminary action eliminates the need for extensive annotation during runtime, reducing the time loss associated with workflow specification while maintaining the productivity gains from optimized genomics data analysis through structured parallel execution.
Data Source
AI summary
In one embodiment, a method of processing a computational workflow comprises receiving a description of a computational workflow. The description comprises a plurality of steps, in which each step has at least one input and at least one output, and further wherein an input from a second step depends on an output from a first step. The description is translated into a static workflow graph stored in a memory, the static workflow graph comprising a plurality of nodes having input ports and output ports, wherein dependencies between inputs and outputs are specified as edges between input ports and output ports. Information about a first set of nodes is then extracted from the static workflow graph and placed into a dynamic graph. A first actionable job is identified from the dynamic graph and executed.