Speaker
Description
Recent advances in manufacturing environments have stimulated the development of intelligent assistants that support human decision making through natural-language interaction, AI-assisted analytics, and seamless access to complex data ecosystems. Meanwhile, Research Infrastructures (RIs) and experimental ecosystem science are becoming increasingly data-intensive, generating large volumes of heterogeneous observations, measurements, models, and metadata. Despite advances in FAIR data management and interoperability, researchers still face challenges in discovering, integrating, and exploiting distributed resources. This study examines how such concepts can be transferred to RIs and experimental ecosystem science.
A conceptual transfer analysis was conducted using recent advances in intelligent assistants, data-centric AI, workflow composition, and human-centered analytics developed in manufacturing environments as the starting point. Particular attention was given to approaches enabling natural-language interaction with distributed data ecosystems and dynamic discovery, composition, and execution of data resources and research workflows. These capabilities were mapped against the characteristics and requirements of modern environmental RIs.
Preliminary results indicate that several architectural principles developed for intelligent assistants are directly transferable to RIs. Based on these principles, a five-layer framework is proposed: (1) Data layer: FAIR datasets, observations, models, and metadata; (2) Knowledge layer: semantic annotations, ontologies, provenance, and domain knowledge; (3) Discovery layer: AI-assisted identification of datasets, tools, workflows, and computing resources; (4) Orchestration layer: dynamic composition and execution of research workflows; (5) Interaction layer: natural-language interfaces providing human-in-the-loop support. In the proposed framework, intelligent assistants function as orchestration and interaction mechanisms between researchers and ecosystem data infrastructures. Researchers formulate information needs in domain language, while the underlying system discovers, combines, and executes appropriate data and modelling resources, exposing results and provenance. Such assistants have the potential to facilitate data discovery, support experiment planning, accelerate knowledge synthesis, and lower technical barriers to the reuse of increasingly complex environmental RIs.
| Are you participating to the "AnaEE Environmental Rising Star Award "? | No |
|---|