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.
Fields of science
- natural sciencescomputer and information sciencesdata science
- natural sciencescomputer and information sciencesknowledge engineering
- social sciencessociologygovernancecrisis management
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencescomputer security
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
151 25 Maroussi
Greece
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Participants (19)
06560 Valbonne
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
78990 Elancourt
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Participation ended
1081 HV Amsterdam
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06560 Valbonne
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
92350 Le Plessis Robinson
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116 36 Praha 1
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67663 Kaiserslautern
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08034 Barcelona
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106 82 ATHINA
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20870 Elgoibar
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06000 Nice
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
19002 Peania
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4042 LIMASSOL
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7034 Trondheim
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2628 CN Delft
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1000 Ljubljana
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08034 Barcelona
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
08034 Barcelona
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
1081 HV Amsterdam
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Partners (1)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
BH12 5BB Poole
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