Dynamic Workflow Execution with Real-Time Data Updates
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Solution Overview
Problem
Existing workflow execution systems face challenges in dynamically updating workflow steps with the latest information during execution, leading to incomplete or outdated data, which can result in inefficient or incorrect execution.
Innovation Solution
The system dynamically selects and updates workflow steps by pulling information from multiple data sources, including vendor updates, user feedback, and social media, and incorporates cognitive learning to provide real-time previews and reasoning for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If workflow steps are executed using static, pre-defined information, then execution speed is improved, but data accuracy and completeness deteriorate
Solution Approach 1:
The workflow execution system dynamically adapts by pulling updated information from data sources during execution. The system transitions from static, pre-defined workflow steps to dynamic steps that can be modified based on real-time information retrieval, ensuring both speed and accuracy are maintained.
Solution Approach 2:
The system performs preliminary information retrieval and validation before executing workflow steps. By pulling and validating information from data sources in advance, the system ensures data accuracy is established before execution begins, preventing information loss during the process.
2Loss of information
If workflow steps are dynamically updated with real-time information, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary information management layer that handles the complexity of real-time data retrieval and validation. This intermediary component manages communication between data sources and workflow execution, abstracting the complexity away from the core workflow logic while ensuring data completeness.
Solution Approach 2:
The system implements feedback mechanisms where execution information is pushed back to data sources and cognitive learning systems. This feedback loop allows the system to learn from execution outcomes and continuously improve information retrieval processes, managing complexity through iterative refinement rather than static complexity.
3Measurement precision
If multiple data sources are queried for workflow information, then information accuracy is improved, but execution time increases
Solution Approach 1:
The system applies partial action by selectively querying only the necessary data sources required for each specific workflow step rather than querying all available data sources. This approach retrieves sufficient information for accurate execution without the overhead of comprehensive queries across all potential sources.
Solution Approach 2:
The system performs preliminary validation and filtering of data sources before execution. By pre-identifying and validating the specific data sources needed for each workflow step, the system avoids unnecessary queries to irrelevant sources, reducing retrieval time while maintaining information accuracy through targeted queries.
Data Source
AI summary
Technical solutions are described for execution of a machine-assisted workflow, the workflow including a sequence of workflow steps. An example method includes pulling a preliminary update for the workflow from a data source and modifying the workflow dynamically. The method further includes selecting a workflow step, pulling an updated information for the workflow step from the data source and modifying the workflow step dynamically using the updated information. The method further includes retrieving, from the data source, first preview information, the first preview information indicative of previews of one or more operations from the updated workflow step. The method further includes to the first preview information, executing the updated workflow step. The method further includes accumulating execution information including second preview information based on the execution and pushing the execution information to the data source upon completion of the workflow step.