News

The Athena Research Center has successfully completed the STELAR project, marking the end of three years of innovation and impactful outcomes. The project, which began in September 2022, was coordinated by the Information Management Systems Institute (IMSI) of the Athena Research Center and set out with the mission to develop a novel platform and toolkit that support data discovery and preparation for machine learning and AI applications particularly within the agrifood data space.

 

At the heart of the project’s achievements lies the Knowledge Lake Management System (KLMS), a powerful and extensible open-source platform for managing large-scale and multidimensional data. The KLMS expands the traditional concept of a data lake by incorporating a semantic layer that provides structure, meaning, and conceptual coherence to data. Through the platform, analysts, researchers, and small and medium-sized enterprises working in data-intensive fields, such as smart agriculture, gain the ability to catalogue, interlink, annotate, transform, and prepare data for machine-learning applications. KLMS supports a wide variety of data types (tabular, spatial, temporal, and textual data) and offers a metadata catalogue and knowledge graph, a workflow-design infrastructure, and a comprehensive suite of tools that help users process, understand, and leverage the information stored within the system.

 

STELAR has laid the foundation for a new generation of data discovery and data interlinking capabilities. Its flexible architecture enables the seamless integration of specialized tools, allowing each organization to adapt the platform to its operational and business-specific needs. This adaptability proved crucial amid the rapid evolution of large language models (LLMs), driving the project to develop features that support natural-language-driven exploration and interaction with complex datasets.

 

The results of STELAR pave the way to new opportunities. Future extensions aim to include autonomous AI agents for navigating and exploring large data collections as well as advanced AI-driven analytics that transform raw data into decision-making insights. Although the project has officially concluded, we continue to build upon its technologies, outcomes, and momentum to further shape a more connected data environment within the rapidly evolving world of AI.