Timed Partial Order Graph Minimization for Workflow Mining
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Solution Overview
Problem
Current workflow mining techniques fail to effectively capture and represent timing constraints between events in workflows, leading to complex and difficult-to-analyze Timed Partial Order Graphs (TPOGs), which hinder easy interpretation and optimization in manufacturing and human-robot collaborative environments.
Innovation Solution
A system and method that generates a Timed Partial Order Graph by identifying partial order graphs from timed traces, reducing redundant constraints, and minimizing the number of clocks required to explain time constraints, resulting in a simplified and correct TPOG structure that accepts input traces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If workflow mining techniques are used to model task order, then the order of tasks is captured, but timing constraints between events are not captured
Solution Approach 1:
The patent combines traditional workflow mining (which captures task order) with timed automata learning (which captures timing constraints) into a unified Timed Partial Order Graph model. This merging allows simultaneous representation of both task sequencing and temporal constraints, resolving the information loss of timing data while maintaining accurate task order modeling.
2Loss of information
If timed automata are used to capture timing constraints, then timing information is represented, but the model becomes complex and difficult to interpret
Solution Approach 1:
The patent extracts and separates timing constraints from the overall workflow model into distinct temporal constraint representations within the Timed Partial Order Graph. By isolating timing information into specific constraint components rather than embedding it throughout the entire model structure, the system maintains timing accuracy while improving model interpretability and reducing apparent complexity.
3Reliability
If SMT solver is used to learn timed automata, then correct timing constraints are obtained, but the computation time becomes exponential
Solution Approach 1:
The patent segments the learning process into distinct phases: first learning the partial order structure from event sequences, then separately learning timing constraints from timestamp data. This segmentation avoids the need for a single complex SMT solver approach, breaking down the exponentially difficult problem into two more manageable sub-problems that can be solved more efficiently while maintaining correctness.
4Loss of time
If genetic algorithm is used to estimate timed automata, then fast computation is achieved, but optimal solutions are not guaranteed
Solution Approach 1:
The patent incorporates feedback mechanisms where the learned Timed Partial Order Graph is validated against the original timed trace data. The model iteratively refines its timing constraints based on how well it explains the observed data, providing correctness guarantees while maintaining computational efficiency. This feedback-driven approach avoids both the slowness of exhaustive SMT solving and the suboptimality of pure genetic algorithms.
Data Source
AI summary
A system is provided for use with a plurality of timed traces for performing a task. The system includes: a data receiver configured to receive the plurality of timed traces; a memory having instructions stored therein; and a processor configured to execute the instructions stored in the memory to cause the system to: store the received plurality of timed traces into the memory; generate a partial order graph of time constraints between all of the plurality of timed traces; generate a transitive reduced partial order graph from the partial order graph, the transitive reduced partial order graph not including redundant time constraints within the partial order graph; and generate a timed partial order graph from the transitive reduced partial order graph, the timed partial order graph having a minimum number of clocks required to explain the time constraints between all of the plurality of timed traces.