Abstract
We present Plato, a novel semantic data cube system leveraging Ontology-Based Data Access technologies. The concept of semantic data cubes, pioneered by Augustin et al., offers a novel approach to managing Earth Observation data, intertwining symbolic high-level concepts with raw numerical values. While data cube infrastructures have gained prominence, Plato stands out by bridging the gap between ontology-driven semantics and multidimensional data storage, enabling users to glean insights from data in a more intuitive and integrated manner. By employing Ontology-Based Data Access methodologies, Plato establishes connections between ontologies capturing geospatial knowledge and underlying data sources, facilitating seamless querying and interpretation. In this paper we describe the software architecture of Plato, we discuss its applications in the context of the DeepCube project and we evaluate its performance with real data from these applications.
Spyros Kondylatos
PhD Candidate
My research interests include Deep Learning, Bayesian Deep Learning, Earth Observation, Natural Hazards, Wildfires Forecasting
Ioannis Prapas
PhD Candidate
My research interests include Deep Learning, Earth Observation, Wildfire Forecasting, Modeling Earth System Dynamics.
Ioannis Papoutsis
Head of Orion Lab
Assistant Professor of Artificial Intelligence for Earth Observation @ NTUA
Adjunct Researcher @ NOA
Earth Observation, Machine Learning, Natural Hazard Monitoring