Cutting-Edge AI for Process Management
Enhancing Declarative Business Process Management Availability Through Generative AI
This research introduces Terpsichora, a novel framework leveraging Large Language Models (LLMs) to address the scarcity of multi-perspective declarative (MP-Declare) process models. It systematically generates diverse, realistic synthetic MP-Declare models, ensuring structural validity and semantic coherence through advanced prompt engineering, constrained generation, and automated validation. This innovation facilitates broader research, education, and practical application in declarative process management.
Driving Enterprise Innovation with Synthetic Models
Terpsichora provides a robust solution to a long-standing challenge, unlocking new possibilities for process model research and application.
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Synthetic MP-Declare Models Generated
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Average Label Compliance Rate
Diverse
Complexity Across Domains
Scalable
and Robust Generation
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Prompt Engineering
Model Coverage & Complexity
Practical Utility & Validation
Mastering LLM Guidance: Effective Prompt Engineering
Our research demonstrates that a synergistic combination of prompt engineering techniques is crucial for guiding LLMs to produce complex, accurate, and semantically meaningful MP-Declare models. Role-play prompting establishes LLMs as expert modelers, ensuring contextually appropriate outputs. Knowledge injection provides precise MP-Declare specifications, significantly reducing hallucination and improving factual accuracy—with experiments showing 205% more correct assertions. Few-shot learning enables LLMs to effectively learn and reproduce complex structural patterns from limited demonstrations. Finally, constrained decoding techniques enforce strict adherence to metamodel specifications, bridging free-form language generation with formal process requirements.
Ensuring Diverse & Realistic Model Generation
Terpsichora's framework successfully generates models spanning diverse business contexts while maintaining high syntactic validity and semantic coherence. This is evidenced by the robust scalability of activity counts, with OpenAI models increasing from 9.90 to 14.88 activities (Batch 1 to Batch 2) and Google models from 9.98 to 15.00 activities, without compromising structural integrity. Consistent density metrics (OpenAI: 0.822–0.888; Google: 1.093–1.111) and constraint variability (OpenAI: 0.768–0.789; Google: 0.702–0.667) across diverse domains confirm the framework's ability to preserve fundamental MP-Declare properties while accommodating contextual variations.
Validated Models for Process Mining & Simulation
The synthetic MP-Declare models generated by Terpsichora prove highly effective for various process analysis tasks. For process mining, successful model rediscovery from generated event logs confirms their operational applicability and ability to encode realistic process behaviors, as exemplified in Figure 3. For conformance checking, the models maintain proper constraint relationships and structural characteristics, supported by consistent density (OpenAI: 0.822–0.888; Google: 1.093–1.111) and separability metrics across different generation approaches. For process simulation, the diverse yet predictable constraint patterns and consistent activity-constraint relationships ensure robust and meaningful simulation scenarios.
205%
More Correct Assertions with Knowledge Injection
Our framework leverages targeted knowledge injection to significantly reduce LLM hallucination, ensuring factual accuracy in generated process models.
Terpsichora Generation Workflow
Request MP-Declare Model Generation with Parameters
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Apply Prompt Engineering Techniques
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Send Engineered Generation Prompt
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Generate Initial Model
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Apply Metamodel Structure
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Validate Model Against Rules
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Pass Validated Model
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Format Model (JSON, DECL)
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Return Generated MP-Declare Model
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Real-World Impact: Revolutionizing BPM Research & Education
Terpsichora's ability to generate diverse, high-quality synthetic MP-Declare models directly addresses the critical challenge of model scarcity in the BPM domain. Researchers can now access rich datasets for validating new algorithms, conducting empirical studies, and advancing process mining techniques without confidentiality concerns. For educators, the framework enables the creation of contextually relevant training materials and test cases, providing practical learning experiences. This capability fosters innovation, accelerates algorithm development, and democratizes access to advanced process modeling methodologies for a wider audience.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Our Structured Approach to AI Implementation
We guide you through a proven methodology to ensure successful integration and maximum impact.
Phase 1: Discovery & Strategy
Comprehensive analysis of existing processes, identification of key declarative modeling needs, and strategic alignment with business objectives.
Phase 2: Framework Customization & Integration
Tailoring Terpsichora to your specific domain, integrating with existing systems, and fine-tuning prompt engineering for optimal model generation.
Phase 3: Model Generation & Validation
Iterative generation of synthetic MP-Declare models, rigorous validation for syntactic correctness, semantic coherence, and practical utility.
Phase 4: Deployment & Continuous Improvement
Deployment of the framework, training your teams, and establishing feedback loops for ongoing model refinement and performance enhancement.
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