creative operations

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

To address the insufficient security and scalability of engineering workflow automation and cross-organizational collaboration in Industry 4.0, this paper proposes an engineering workflow management approach integrating Asset Administration Shells (AAS) with BPMN. We innovatively design a distributed, write-on-copy AAS infrastructure to ensure data consistency and access security, and develop a lightweight workflow engine prototype supporting native AAS operations, enabling automatic mapping and execution of BPMN processes onto AAS interactions. This method unifies digital twin representation, asset modeling, and business process logic, thereby significantly enhancing standardization of engineering data exchange, end-to-end process traceability, and multi-stakeholder collaboration efficiency. Experimental evaluation demonstrates the system’s feasibility for secure inter-organizational coordination and its horizontal scalability across heterogeneous industrial environments.

本文提出了一种新的资产管理壳成熟度概念，以解决资产管理壳实例比较缺乏系统方法的问题，通过文献推导和示例应用，支持数字孪生工程中的评估和发展。

本文提出了一种基于集合论的操作方法和AAS适用性模型，以解决不同AAS实例在结构、内容及完整性上的差异问题，从而提高其在特定应用场景中的可比性和适用性。

This work addresses the high cost and limited scalability of manually constructing high-quality data products—such as question-answer pairs and database views—which traditionally rely on domain experts. To overcome these challenges, the authors propose an automated optimization framework based on a multi-agent system, introducing for the first time an agent control center architecture. This architecture continuously identifies user queries, monitors multidimensional quality metrics, and integrates a human-in-the-loop mechanism to ensure observability and iterative refinement of data assets. By maintaining human oversight while automating core optimization processes, the approach substantially reduces manual effort and significantly enhances the relevance, coverage, usability, and trustworthiness of data products.

Current AI creativity support tools generate low-level interaction logs—such as clicks and parameter adjustments—that poorly capture users’ creative intent, limiting agents’ understanding of the design process. This work proposes a novel approach that transforms raw, noisy logs into structured, high-level behavioral workflow graphs by abstracting semantic action tokens like MODIFY_Prompt and GENERATE_Image. For the first time, this method enables a meaningful mapping from low-level interactions to interpretable creative workflows. Through log parsing, behavioral abstraction, and sequence modeling, it produces a structured representation amenable to downstream mining and probabilistic reasoning. This representation lays the foundation for “process-aware agents” capable of offering design suggestions or explaining decisions grounded in users’ historical creative behavior.

This work addresses the unreliability of developer productivity dashboards, which often stems from ad hoc scripts that introduce undetected silent data gaps, eroding organizational trust. To resolve this, we propose a robust ELT pipeline grounded in DAG-based orchestration and the Medallion architecture, decoupling data extraction from transformation to preserve the immutability of raw data. Our approach introduces a state-driven dependency scheduling mechanism and, for the first time, treats metric pipelines as production-grade distributed systems. We emphasize the critical role of immutable raw history in enabling reliable metric redefinition. This methodology significantly enhances data reliability and freshness while effectively eliminating silent failures, thereby restoring organizational confidence in DevOps metrics.

This study addresses the challenge of automating workflows in complex industries—such as logistics, healthcare, and construction—where processes are fragmented across heterogeneous tools and involve multi-party collaboration. The work proposes orchestration as a core abstraction to enable effective automation by dynamically coordinating multi-step tasks, enforcing domain-specific constraints, managing human approvals, and integrating legacy systems. It introduces the novel concept of “orchestration bottlenecks” and develops a theoretical framework that unifies multi-agent systems, workflow modeling, constraint reasoning, and human–AI collaboration, while exposing critical gaps in current multi-agent approaches at the orchestration level. Based on distinct sources of operational friction across domains, the paper advocates for targeted architectural safeguards—such as constraint enforcement or explainability—and phased implementation strategies to provide actionable pathways for automation in complex operational environments.

This study addresses the lack of systematic understanding regarding how GitHub Actions workflows are used in real-world scenarios, how developers respond to workflow failures, and how these practices relate to project characteristics. Combining large-scale quantitative analysis of 258,300 workflow runs with qualitative case studies across 21 diverse repositories, this work identifies three typical patterns developers employ to handle workflow failures and uncovers a “configuration–usage gap”—where YAML configurations exist but workflows remain effectively unused. Furthermore, the study empirically validates five hypotheses linking project features to workflow usage intensity, revealing a significant positive correlation between high usage intensity and low failure rates. These findings provide actionable empirical evidence for improving CI/CD practices.

This study addresses the challenges of high latency, unstable concurrency, and security risks faced by large language model (LLM) agents in automating asset lifecycle management within Industry 4.0. The authors propose a Plan-then-Execute architecture that generates verifiable workflow graphs and integrates a topology-aware parallel scheduling mechanism to enable controlled inference overlap while ensuring functional correctness and security. Key technical contributions include topological-sort-based multi-agent scheduling, structured context pruning, dependency-aware concurrency control, and graceful degradation under fault injection. Evaluated on the AssetOpsBench benchmark, the system reduces median end-to-end latency by 1.6× (up to 1.8× for highly parallel tasks) and cuts inference overhead by approximately 30% through context pruning, all while maintaining stable task completion rates and output quality.