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EXPeriment driven and user eXPerience oriented analytics for eXtremely Precise outcomes and decisions

Project description

A new framework for experimentation-driven analytics

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 ExtremeXP project 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 with computation variants, promoting a human-centered, experimentation-based approach to AI and complex analytics. The project will perform five pilot demonstrations.

Objective

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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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL4-2022-DATA-01

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Coordinator

ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 814 672,00
Address
ARTEMIDOS 6 KAI EPIDAVROU
151 25 Maroussi
Greece

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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 814 672,00

Participants (19)

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