Extreme data characteristics represent a challenge for advanced data-driven analytics and decision-making in critical domains such as crisis management, predictive maintenance, mobility, public safety and cyber-security. Data-driven insights must be timely, accurate, precise, fit-for-purpose and reliable, considering and learning from user intents and preferences. The EU-funded project, ExtremeXP, will create a next-generation decision support framework that integrates novel research from big data management, machine learning, visual analytics, explainable ΑΙ, decentralised trust, and knowledge engineering. The framework will aim at optimising the properties of complex analytics processes (e.g., accuracy, time-to-answer, specificity, recall, precision, resource consumption) by associating different user profiles to computation variants, promoting a human-centered, experimentation-based approach to AI and complex analytics. The project will perform five pilot demonstrations.

As weather-related disasters are on the rise, disaster management is more important than ever. The use of advanced technologies is considered essential. The EU-funded TEMA project will improve natural disaster management (NDM) by automating precise semantic 3D mapping and disaster evolution prediction. To that end, it will analyse and combine many heterogeneous extreme data sources and develop an integrated, ground-breaking NDM platform. TEMA will focus on real-time semantic extraction from multiple heterogeneous data modalities and sources and construct a semantically annotated 3D disaster area map, which will be constantly updated, allowing the personnel to visualise and evaluate different response strategies through simulation.

Weather and health emergencies and maritime applications collect live streams of extreme scale data that reach high speeds and volumes and possess highly volatile statistical properties, incorporating uncertainties and diverse modalities through a multitude of sources. The EU-funded CREXDATA project will develop a generic platform for real-time critical situation management including flexible action planning and agile decision making over streaming data of extreme scale and complexity. The project will develop the algorithmic apparatus, software architectures and tools for federated predictive analytics and forecasting under uncertainty. The envisioned framework boosts proactive decision making providing highly accurate and transparent short- and long-term forecasts, explainable via advanced visual analytics and accurate, real-time, augmented reality facilities.