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ExtremeXP

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EXPeriment driven and user eXPerience oriented analytics for eXtremely Precise o...

EXPeriment driven and user eXPerience oriented analytics for eXtremely Precise outcomes and decisions Extreme data characteristics (volume, speed, heterogeneity, distribution, diverse quality, etc.) challenge the state-of-the-art data-driven analytics and decision-making approaches in many critical domains such as crisis managemen... ver más

31/12/2025

Líder desconocido

10M€

Presupuesto del proyecto: 10M€

Líder del proyecto Líder desconocido

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-11-16

HORIZON-CL4-2022-DATA-01-05: Extreme data mining, aggregation an... ExpectedOutcome:Proposal results are expected to contribute to the following expected outcomes:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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1 Participantes

STICHTING AMSTERDAM UMC

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Duración del proyecto: 37 meses Fecha Inicio: 2022-11-16
Fecha Fin: 2025-12-31

Líder del proyecto Líder desconocido

Presupuesto del proyecto 10M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Extreme data characteristics (volume, speed, heterogeneity, distribution, diverse quality, etc.) challenge the state-of-the-art data-driven analytics and decision-making approaches in many critical domains such as crisis management, predictive maintenance, mobility, public safety, and cyber-security. At the same time, data-driven insights need to be extremely timely, accurate, precise, fit-for-purpose, and trustworthy, so that they can be useful. ExtremeXP will handle the complexity of matching extreme needs with complex analytics processes (i.e., processes that involve and combine ML, data analysis, simulation and visualization components) by placing the end user at the centre of complex analytics processes and relying on user intents and running experiments (i.e., trial and error) to prune the vast solution space of possible analytics workflows and configurations i.e., variants. Its main goal is to create a next generation decision support system that integrates novel research results from the domains of data integration, machine learning, visual analytics, explainable AI, decentralised trust, knowledge engineering, and model-driven engineering into a common framework. The overarching idea of the framework is to optimise the properties of a complex analytics process that the end user cares about (e.g., accuracy, time-to-answer, specificity, recall, precision, resource consumption) by associating user profiles to computation variants. The framework is envisioned as modular and extensible, orchestrating different services around an Experimentation Engine: Analysis-aware Data Integration, Extreme Data & Knowledge Management, User-driven AutoML, Transparent & Interactive Decision Making, and User-driven Optimization of Complex Analytics. The framework will be validated in five pilot demonstrators.

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