technology scouting

Existing RTI policy monitoring tools suffer from inefficient information acquisition and inadequate dynamic tracking capabilities. This study proposes a methodology for developing an open-source web-based system dedicated to Research, Technology, and Innovation (RTI) policy monitoring. It employs role-driven requirements engineering to precisely elicit heterogeneous stakeholder needs and introduces a novel modular architectural paradigm centered on a user-configurable dashboard, with strict separation across presentation, service, and data layers. The approach integrates open-data interoperability standards and interactive visualization techniques. As key contributions, the work delivers a reusable RTI monitoring system architecture specification and a standardized dashboard requirements template. These artifacts were empirically validated through deployment in the Austrian RTI Monitor—a national-scale platform enabling cross-departmental, real-time indicator tracking and evidence-informed policy coordination—thereby substantially enhancing the timeliness, accessibility, and scalability of RTI policy monitoring.

This work addresses the challenge of quantifying the academic impact of commercial engineering software such as Ansys Granta, which is hindered by inconsistent citation practices and rapidly growing publication volumes. We propose the first reproducible, semi-automated framework that integrates DOI and citation parsing, expert annotation, and a relational database (Ansys Granta MI Enterprise) to transform heterogeneous usage evidence into a structured knowledge base. As of September 2025, the framework has compiled a multi-source literature repository comprising over 1,100 manually verified records, enabling rapid retrieval, systematic review reproduction, and technology landscape scanning. The resulting knowledge base reveals dominant application domains, key contributing institutions, and integration patterns within CAD/CAE/FEM environments, thereby facilitating systematic tracking and analysis of the long-term technical influence of commercial engineering software.

Spatial process mining for physical entities in digital twins remains challenging, particularly in complex manufacturing environments such as modular production lines, where manual domain expertise is often required for deviation diagnosis. Method: This paper proposes an automated, domain-expert-free deviation attribution method. It employs overhead cameras for active perception to generate event logs and constructs a spatiotemporal partitioning model. An improved HITS-based event-node ranking mechanism is introduced to automatically localize root causes of deviations. Additionally, a novel Gantt-style process representation and visualization framework is proposed, specifically designed for spatial cells. Contribution/Results: The approach enables bird’s-eye-view dynamic process monitoring, achieving high interpretability while significantly improving both the efficiency and accuracy of anomaly diagnosis. Experimental evaluation validates its effectiveness in real-world industrial settings.

Business process optimization remains challenging due to fragmented methodologies across process mining, predictive process monitoring, and process-aware recommendation—each operating in isolation without a unified theoretical foundation or integration framework. Method: This paper proposes a closed-loop optimization framework that systematically integrates Alpha algorithm/Inductive Miner for process discovery, LSTM/Transformer for runtime prediction, collaborative filtering/graph neural networks for action recommendation, and explainable AI (XAI) for interpretability—enabling automated bottleneck identification, anomaly forecasting, and prescriptive optimization from event logs. Contribution/Results: We establish the first unified conceptual boundary, evolutionary taxonomy, and synergy paradigm across the three domains; construct a comprehensive classification schema covering 120+ studies; clarify application scopes and standardized evaluation benchmarks; and deliver an industrially actionable methodology selection guide with validated deployment pathways.

This work addresses the challenge scientists face in efficiently transforming raw sensor data streams into actionable insights across edge-cloud infrastructures, hindered by the need for cross-domain expertise to manage heterogeneous systems and emerging platforms such as DPUs, which impedes rapid prototyping. To overcome this barrier, the authors propose a novel paradigm that integrates pattern-based workflow engineering with AI-assisted development. Implemented on the FABRIC testbed using the Pegasus workflow system and exemplified by the Orcasound hydrophone workflow, this approach enables swift construction of applications for air quality, seismic, and soil moisture monitoring. The framework supports modular extensibility and edge deployment, substantially lowering the barrier for non-expert users to iteratively develop distributed applications. Empirical validation across multiple use cases demonstrates its effectiveness in enhancing development efficiency, accelerating prototyping cycles, and accumulating practical deployment experience.

This work addresses the complexity of developing edge-to-cloud sensor applications, which typically requires cross-domain collaboration and hinders efficient transformation of raw data into actionable insights. To streamline this process, the authors propose an intent-driven, AI-assisted rapid development methodology that integrates reusable workflow patterns with intelligent configuration, enabling seamless edge adaptation and deployment without code rewriting. Built upon the Pegasus workflow system and deployed on the FABRIC testbed, the approach supports heterogeneous edge resources such as BlueField-3 DPUs and Raspberry Pi devices. Users can construct multi-stage sensing applications within 1–1.5 days, and the framework’s robustness and portability have been validated through real-world deployments in air quality, seismic activity, and soil moisture monitoring scenarios.

Existing datasets of scientific ideation trajectories struggle to comprehensively capture the full research process—from literature exploration and tool utilization to the evolution of intermediate artifacts and final proposals. This work proposes a reverse-to-forward synthesis mechanism that emulates the uncertainty, evidence integration, and phased convergence characteristic of real scientific inquiry through a Generator–Advisor architecture. By leveraging action–observation–editing sequence modeling, context-aware verification, and process-level supervision, the approach generates multi-turn trajectories aligned with authentic research practices, starting from high-quality papers and proposals. The study yields the first trajectory dataset spanning the complete scientific workflow and establishes a generalizable paradigm for synthesizing process-supervised data for scientific agents.

This work addresses the challenge of effectively integrating process mining results into early-stage requirements engineering by proposing an automated modeling approach tailored to Use Case Maps (UCMs) within the ITU-T URN standard. By extending the PM4Py library, the authors develop the first process mining pipeline that treats UCMs as first-class outputs, supporting configurable actor mapping and nested hierarchical decomposition. The method enables high-fidelity bidirectional interoperability with the jUCMNav tool. Empirical evaluation on both public and synthetic event logs demonstrates its capability to accurately represent behavioral models across multiple abstraction levels, thereby advancing process mining as a practical enabler for model-driven requirements engineering.