Dynamic Directed Graph Workflow Management
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Solution Overview
Problem
Current electronic workflow management systems face challenges such as manual intervention, complex code bases, inaccuracies, and limited applicability due to oversimplifications, which hinder flexibility and processing efficiency.
Innovation Solution
A dynamic directed graph is employed to electronically manage workflows, featuring modular nodes with executable code for data collection and retention, allowing for dynamic updates and temporal navigation, enabling improved flexibility and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual intervention is used in workflow management, then flexibility is maintained, but productivity decreases due to large amounts of manual work
Solution Approach 1:
The workflow management system transitions from static manual processes to dynamic automated execution. The system automatically executes workflow definitions when triggered by events, dynamically allocating resources and adjusting execution based on runtime conditions while maintaining the ability to modify workflow definitions without code changes.
Solution Approach 2:
Manual mechanical workflow execution is replaced by an automated electronic system. The system uses event triggers and automated rule evaluation to execute workflows, substituting human manual intervention with automated computing processes that maintain flexibility through configurable workflow definitions.
2Productivity
If automated workflow systems are implemented, then productivity increases, but device complexity increases due to complex code bases
Solution Approach 1:
The workflow system is segmented into distinct components: workflow definitions (configurable data), event triggers, execution engine, and resource management. This segmentation allows each component to be independently managed, reducing overall system complexity while maintaining automated productivity.
Solution Approach 2:
Instead of writing custom code for each workflow, the system uses configurable workflow definition templates that can be copied and adapted. Workflow definitions are stored as data structures rather than code, allowing replication and modification without increasing code base complexity.
3Ease of operation
If workflow definitions are oversimplified to improve ease of use, then ease of operation increases, but measurement precision decreases due to inaccuracies in workflow representation
Solution Approach 1:
The system allows workflow definitions to be modified by changing parameters in configurable data structures rather than altering code. This enables precise representation of complex workflows while maintaining ease of operation through parameter-based configuration and validation rules.
4Reliability
If historical data is retained for all workflow elements, then reliability improves for temporal navigation, but loss of substance increases due to memory allocation requirements
Solution Approach 1:
Historical data retention is applied selectively rather than uniformly. The system retains historical values for workflow elements that require temporal navigation while using efficient storage for elements that do not require history, optimizing memory allocation based on local requirements of each workflow component.
Data Source
AI summary
A set of nodes are organized into a graph to represent a workflow to enable the dynamic and directed management of that workflow in a decentralized system. Each node maintains a value necessary for execution of the workflow, and includes code to populate that value. A workflow agent manages the population of the values according to an identified dependency structure for the nodes relative to the workflow. As changes are made to the workflow, the workflow agent ensures that values and dependencies of the nodes stay up-to-date. Each node retains historic values, which enables the workflow agent to query several states of the workflow throughout time as changes are made thereto. The dynamic management of the nodes improves the responsiveness of the system to changes, thereby improving computational efficiency.


