ExtremeXP

Description:

ExtremeXP 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. ExtremeXP proposes a new paradigm for data analytics, which consists of experimentation-driven analytics to provide accurate, precise, fit-for-purpose, and trustworthy data-driven insights via evaluating different complex analytics variants, considering end users’ preferences and feedback in an automated way. The ambition is to provide capabilities for learning from experimentation to predict user requirements, profile the user, and proactively generate the accurate analytics workflow towards more precise outcomes and personalized insights for decision making and focusing on the user experience, requirements, and needs and putting him in the centre of the decision-making process.

The i2CAT Foundation will actively participate in Work Packages 3 and 6 within the project. Within the WP3 -Complex analytics methods and techniques-, i2CAT leads Task 3.3: Simulation-driven data augmentation, oriented at implementing algorithms and processes to deal with unbalanced or incomplete datasets, being used to train ML models using data augmentation and simulation techniques. Within WP6 -Complex analytics methods and techniques-, i2CAT leads Task 6.1: Requirements of use cases: intents, data analytics workflows, data sources, datasets. This task will investigate each use-case’s requirements and issue technical designs of the use-case pilots while contributing functional and technical expectations over the ExtremeXP project. For each use case, this process includes the selection of adequate datasets, domain modelling, the variability point identification, the specification of the experiment models, the elicitation of user intents, and the determination of the technical settings for evaluation. Finally, the i2CAT Foundation oversees the Use Case 2: Increased Cybersecurity situation awareness for efficient threat mitigation, aimed at designing a Security Information Management System (SIEM) framework demonstrator implementing a multimodal threat detection and classification feature trained with cybersecurity expert skills. This demonstrator will exploit the ExtremeXP framework to increase the recognition of cyber threats by featuring efficient and accurate AI on extreme data, contributing to the emergency management of information systems in several verticals. The demonstrator will be implemented and evaluated in the AI4Cyber testbed provided by i2CAT. It comprises a complete stack of tools for project development and management in the field of cybersecurity and artificial intelligence, including user behaviour analysis, threat analysis, threat profiling and modelling.

Estimated impact:

• Improve European leadership in the global data economy.
• Maximise social and economic benefits from the wider and more effective use of data.
• Reinforce Europe’s ability to manage urgent societal challenges (e.g. data for crisis management, digital for clean energy).
• Improved the robustness and resilience of digital infrastructure in Europe.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or [name of the granting authority]. Neither the European Union nor the granting authority can be held responsible for them.