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

Informations projet

ExtremeXP

N° de convention de subvention: 101093164

DOI

10.3030/101093164

Date de signature de la CE 16 Novembre 2022

Date de début 1 Janvier 2023

Date de fin 28 Février 2026

Financé au titre de

• Digital, Industry and Space

Coût total

€ 10 011 820,00

Contribution de l’UE

€ 10 011 820,00

Coordonné par

ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIS
Greece

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Demonstrators, pilots, prototypes (2)

Core framework services - Data and knowledge management

This deliverable will document the core framework services responsible for (a) the secure and distributed management of datasets, knowledge assets and experimentation-based learning outcomes and (b) defining and enforcing appropriate data authorisation by considering contextual information of the requestor, the artefact to be accessed and processed and their environment. It will include prototype libraries and/or tools and will document their implementation and usage information.

Data selection, integration, and simulation services

This deliverable will provide the first versions of (a) automated dataset selection strategies and feature augmentation methods, that will recommend the datasets that are fit-for-purpose for the current analytics task; (b) Analysis aware data integration and quality assurance services, that will support the interactive application of cleaning, interlinking, and enrichment methods to data gathered from various dispersed sources; (c) Data augmentation and simulation techniques, that will allow to detect the types and ranges of data that are missing in the datasets, in order to generate new data entries to balance the datasets with various augmentation techniques, and or deploy simulation models to produce new data. It will include prototype libraries and/or tools and will document their implementation and usage information.

Documents, reports (2)

Initial architecture, languages and models for complex experiment-driven analytics

This deliverable will report on the design the architecture of the ExtremeXP framework, as well as the modelling language and the underlying models that will support experiment-driven analytics. It will provide the specifications of a domain specific modelling language, as well as knowledge graphs for semantically representing experiments. Finally, it will describe the architecture of the framework, which will comprise several independent, self-contained, and elastic core services that can be used to store knowledge assets from experiments, collect evaluation data including user feedback, plan experiments, and enact them (either locally or on remote systems) using virtualized resources and serverless functions.

Use case requirements

This deliverable will report on the requirements elicited from each use-case and will 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, the 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.

Publications

Conference proceedings (24)

Optimizing Data Analytics Workflows through User-driven Experimentation

Auteurs: Keerthiga Rajenthiram
Publié dans: 3rd International Conference on AI Engineering – Software Engineering for AI (CAIN 2024), 2024
DOI: 10.1145/3644815.3644971

METIS: AN OPEN-ARCHITECTURE FOR BUILDING AI-READY CLOUD PLATFORMS – APPLICATION TO FOSTER RESEARCH ON HYDROLOGICAL MODELING

Auteurs: Vincent GAUDISSART, Yasmine BOULFANI, Kevin LARNIER, Gwendoline STEPHAN, Jacques COVES and Christophe TRIQUET
Publié dans: Proceedings of the 2023 conference on Big Data from Space (BiDS’23), Numéro KJ-05-23-390-EN-N, 2023, ISBN 978-92-68-08696-4
Éditeur: Joint Research Centre (European Commission)
DOI: 10.2760/46796

Capturing Analytical Intents from Text

Auteurs: Gerard Pons, Miona Dimic, Besim Bilalli
Publié dans: 2020
Éditeur: Springer
DOI: 10.1007/s10844-020-00604-x

There is no Data Science without Data Governance: a Proposal Based on Knowledge Graphs

Auteurs: Besim Bilalli, Petar Jovanovic, Sergi Nadal, Anna Queralt, Oscar Romero
Publié dans: DOLAP 2024: 26th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, 2024, ISSN 1613-0073
Éditeur: CEUR

Online ML Self-adaptation in Face of Traps

Auteurs: Topfer, Michal; Plasil, Frantisek; Bures, Tomas; Hnetynka, Petr; Krulis, Martin; Weyns, Danny
Publié dans: Proceedings of ACSOS 2023, Toronto, Canada, 2023
DOI: 10.48550/arxiv.2309.05805

Non-Expert Level Analysis of Self-Adaptive System

Auteurs: Claudia Raibulet and Xiaojun Ling
Publié dans: ASOCA2023@ICSOC 2023, 2023
Éditeur: Springer, Singapore
DOI: 10.1007/978-981-97-0989-2_8

Flash flood modeling and in urban areas using High Resolution hydrodynamic model and machine learning models

Auteurs: K. Larnier, J. Coves, G. Stephan and L. Dumas
Publié dans: Fifth Space for Hydrology Workshop, 2024
Éditeur: ESA

An Empirical Performance Comparison between Matrix Multiplication Join and Hash Join on GPUs

Auteurs: Wenbo Sun; Asterios Katsifodimos; Rihan Hai
Publié dans: 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW), 2023, ISBN 979-8-3503-2245-3
Éditeur: IEEE
DOI: 10.1109/ICDEW58674.2023.00034

Amalur: Data Integration Meets Machine Learning

Auteurs: Hai, R. (author); Koutras, C. (author); Ionescu, A. (author); Li, Z. (author); Sun, W. (author); van Schijndel, Jessie (author); Kang, Yan (author); Katsifodimos, A (author)
Publié dans: Crossref, 2023
DOI: 10.48550/arxiv.2205.09681

Discovery of Semantic Non-Syntactic Joins

Auteurs: Marc Maynou, Sergi Nadal
Publié dans: DOLAP 2024: 26th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, 2024, ISSN 1613-0073
Éditeur: CEUR

Evolvability of Machine Learning-based Systems : An Architectural Design Decision Framework

Auteurs: Joran Leest, Ilias Gerostathopoulos, Claudia Raibulet
Publié dans: 2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C), 2023
Éditeur: IEEE
DOI: 10.1109/ICSA-C57050.2023.00033

Towards a Reference Component Model of Edge-Cloud Continuum

Auteurs: Danylo Khalyeyev, Tomáš Bureš, and Petr Hnětynka
Publié dans: 20th IEEE International Conference on Software Architecture (ICSA 2023), 2023
Éditeur: IEEE
DOI: 10.1109/ICSA-C57050.2023.00030

Mitigating Data Sparsity in Integrated Data through Text Conceptualization

Auteurs: Md Ataur Rahman, Sergi Nadal, Oscar Romero, Dimitris Sacharidis
Publié dans: 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024
Éditeur: IEEE
DOI: 10.1109/ICDE60146.2024.00269

Auditing for Spatial Fairness

Auteurs: Sacharidis, Dimitris; Giannopoulos, Giorgos; Papastefanatos, George; Stefanidis, Kostas
Publié dans: 2023
DOI: 10.48550/arxiv.2302.12333

MAPE-K based Guidelines for Designing Reactive and Proactive Self-Adaptive Systems

Auteurs: Hendrik Jilderda and Claudia Raibulet
Publié dans: Post Proceedings of the ECSA 2023 Workshops, in press, 2023
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-66326-0_4

Adaptive Strategies Metric Suite

Auteurs: Koen Kraaijveld and Claudia Raibulet
Publié dans: 2024
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-64182-4_14

An Approach for Intelligent Behaviour-Based Threat Modelling with Explanations

Auteurs: S. Preetam, M. Compastié, V. Daza, and S. Siddiqui,
Publié dans: 2023, ISSN 2832-2231
Éditeur: IEEE
DOI: 10.1109/NFV-SDN59219.2023.10329587

AutoFeat: Transitive Feature Discovery over Join Paths

Auteurs: Andra Ionescu, Kiril Vasilev, Florena Buse, Rihan Hai, Asterios Katsifodimos
Publié dans: 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024
Éditeur: IEEE
DOI: 10.1109/ICDE60146.2024.00150

Model Selection with Model Zoo via Graph Learning

Auteurs: Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai
Publié dans: 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024
Éditeur: IEEE
DOI: 10.1109/ICDE60146.2024.00088

Early Stopping of Non-productive Performance Testing Experiments Using Measurement Mutations

Auteurs: Milad Abdullah, Lubomír Bulej, Tomáš Bureš, Vojtěch Horký, Petr Tůma
Publié dans: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2023, ISBN 979-8-3503-4235-2
Éditeur: IEEE
DOI: 10.1109/SEAA60479.2023.00022

HYPPO: Using Equivalences to Optimise Pipelines in Exploratory Machine Learning

Auteurs: Antonis Kontaxakis, Dimitris Sacharidis, Alkis Simitsis, Alberto Abelló, Sergi Nadal:
Publié dans: 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024
Éditeur: IEEE
DOI: 10.1109/ICDE60146.2024.00024

Expert-Driven Monitoring of Operational ML Models

Auteurs: Leest, Joran; Raibulet, Claudia; Gerostathopoulos, Ilias; Lago, Patricia
Publié dans: International Conference on Software Engineering (ICSE), 2024, ISBN 979-8-4007-0217-4
DOI: 10.48550/arxiv.2401.11993

Visualization-aware Time Series Min-Max Caching with Error Bound Guarantees

Auteurs: Stavros Maroulis, Vassilis Stamatopoulos, George Papastefanatos, Manolis Terrovitis
Publié dans: 50th International Conference on Very Large Databases (VLDB 2024), 2024
Éditeur: VLDB Endowment
DOI: 10.14778/3659437.3659460

Controlling Automatic Experiment-Driven Systems Using Statistics and Machine Learning

Auteurs: Milad Abdullah
Publié dans: Postproceedings of ECSA 2022 Tracks and Workshops, 2023
DOI: 10.1007/978-3-031-36889-9_9

Peer reviewed articles (2)

Data Lakes: A Survey of Functions and Systems

Auteurs: Rihan Hai; Christos Koutras; Christoph Quix; Matthias Jarke
Publié dans: IEEE Transactions on Knowledge and Data Engineering, 2023, ISSN 1041-4347
Éditeur: IEEE
DOI: 10.48550/arxiv.2106.09592

Information Systems

Auteurs: Joseph Giovanelli, Besim Bilalli, Alberto Abelló, Fernando Silva-Coira, Guillermo de Bernardo
Publié dans: Information Systems, Numéro 120, 2024, ISSN 0306-4379
Éditeur: Elsevier Science & Technology
DOI: 10.1016/j.is.2023.102314