#process-mining News & Analysis

6 articles tagged with #process-mining. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AINeutralarXiv – CS AI · Jun 105/10

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SCOPE: Sequential Causal Optimization of Process Interventions

Researchers introduce SCOPE, a new machine learning approach for Prescriptive Process Monitoring that optimizes sequential business interventions using causal inference rather than simulation-based reinforcement learning. The method addresses a critical gap in existing systems by accounting for how multiple interventions interact over time while working directly with observational data, demonstrated through testing on synthetic and semi-synthetic datasets.

AINeutralarXiv – CS AI · Jun 96/10

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Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (and Fail) Red Team Attacks

Researchers applied process mining techniques to red team attack logs against large language models, revealing that standard attack success rate metrics mask critical differences in how models defend themselves. GPT-OSS 120B exhibits a near-absorbing refusal state, while Llama 3.3 70B shows multiple escape routes from refusal, with mutator effectiveness varying significantly across models.

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AINeutralarXiv – CS AI · Jun 26/10

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Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways

Researchers applied process mining techniques to COVID-19 clinical data to optimize hospital workflow management, revealing variability in emergency department procedures and identifying outcome differences based on patient age and ICU exposure. The study demonstrates how data-driven process analysis can inform evidence-based hospital governance and resource allocation.

AINeutralarXiv – CS AI · May 275/10

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Developing a Totally Unimodular Linear Program for Optimal Conformance Checking: When and Why It Complements A*

Researchers propose a totally unimodular linear programming approach to conformance checking in process mining as an alternative to A* search algorithms. Testing on 2.1 million instances reveals complementary performance characteristics, with the LP method achieving 38.6% average runtime improvements for longer traces with deviations while A* excels on short, well-conforming traces.

AINeutralarXiv – CS AI · Apr 146/10

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Ambiguity Detection and Elimination in Automated Executable Process Modeling

Researchers have developed a framework to detect and eliminate ambiguities in natural-language specifications converted to executable BPMN process models by large language models. The method identifies behavioral inconsistencies through KPI analysis, diagnoses gateway logic problems, and repairs source text through evidence-based refinement, reducing variability in regenerated model behavior.

AINeutralarXiv – CS AI · Mar 176/10

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PMAx: An Agentic Framework for AI-Driven Process Mining

Researchers have developed PMAx, an autonomous AI framework that democratizes process mining by allowing business users to analyze organizational workflows through natural language queries. The system uses a multi-agent architecture with local execution to ensure data privacy and mathematical accuracy while eliminating the need for specialized technical expertise.