Workflow Element Prediction Using Contextual Transition Matrices
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Solution Overview
Problem
Conventional workflow development environments lack effective assistance for developers in predicting and completing workflow elements, relying solely on syntax and language features, which limits accuracy and consistency in workflow development.
Innovation Solution
A method and system for predicting workflow elements by analyzing the current workflow configuration and previously developed workflows, using contextual similarity, keyword analysis, and weighted combination of transition matrices to provide relevant element choices with associated probabilities, enhancing auto-completion capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional auto-completion techniques are used in workflow development environments, then syntax and language features can be supported, but accuracy and consistency in predicting workflow elements are limited
Solution Approach 1:
The system pre-processes and stores workflow transition information from previously developed workflows in a database before they are needed for prediction. This preliminary action includes extracting transition patterns and storing them in structured formats (e.g., matrices), so that when a developer is working on a new workflow, the system can quickly retrieve and apply relevant patterns without performing complex analysis in real-time, thereby improving prediction accuracy while managing system complexity
Solution Approach 2:
The patent introduces an intermediary component (workflow prediction engine with transition matrices) that mediates between the raw workflow data and the auto-completion suggestions. This intermediary processes the workflow transition information, identifies patterns, and transforms them into predictive models that can assist developers, thereby enhancing accuracy without directly exposing the full complexity of the analysis system to the user
2Reliability
If workflow transition information from multiple sources is combined to improve prediction accuracy, then element choice accuracy increases, but system complexity and computational requirements increase
Solution Approach 1:
The patent merges workflow transition information from multiple sources (previously developed workflows, current workflow configuration) into a unified predictive model. It combines transition patterns from different workflows by aggregating their transition matrices and applying weighting factors to reflect the relevance and reliability of each source, thereby improving consistency and reliability of predictions while managing the complexity through systematic integration methods
3Loss of information
If contextual similarity analysis is performed on previously developed workflows to select relevant examples, then prediction relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary indexing and categorization of previously developed workflows based on their contextual characteristics (e.g., workflow type, domain, structure) before they are needed for prediction. This pre-processing creates a structured repository where workflows are organized and tagged, enabling the system to quickly retrieve contextually similar workflows without performing full similarity analysis on all stored workflows each time, thus reducing processing time while maintaining relevance
Solution Approach 2:
The patent applies local quality by focusing contextual similarity analysis only on specific relevant aspects of workflows rather than analyzing entire workflows in detail. It identifies and analyzes key contextual features (e.g., workflow stage, element types, transition patterns) that are most relevant to the current prediction context, thereby reducing computational overhead while maintaining the relevance of the retrieved examples
Data Source
AI summary
This technology may be used to predict elements for workflow development. A current configuration of the new workflow is received, and workflow element choices for a next element to be added to the new workflow are determined a long with a respective probability of relevance associated with each of the workflow element choices. The workflow element choices are arranged in accordance with the respective probability of relevance associated with each of the workflow element choices for selection by the user. The workflow element choices may be determined using the current configuration of the new workflow and previously-developed workflows stored in storage accessible by the processing system.