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

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

Communication & dissemination, and standardisation activities

This deliverable will report on the communication activities , as well as project-related materials for outside-facing communication (collaterals) and supporting resources. It will also report the dissemination of research and technical results produced by the project. The main elements of the dissemination plan will be the tasks, responsible partners, materials used, audience addressed and timing; and it will be updated annually. ExtremeXP will build on emerging standards, internationalising the results beyond European borders. The existing participation of partners in related fora and standardisation initiatives will be leveraged. Finally, the project learnings will also be shared with relevant standardisation bodies (SDOs), to inform the development of standards emerging from the different tasks and the use cases.

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.

Exploitation, business model analysis and innovation perspectives

This deliverable will report on the development of the exploitation plan and strategies targeting relevant stakeholders and market segments. These will include the following activities: organize hands-on exploitation workshops with invited third parties to check progress, provide critical feedback and support market outreach; advise and support the consortium for IPR management issues and protection when required (patent, copyright, etc.) and for handling the open source code, the long-term governance of the open source project and the implementation of recognized open source project management best practices; write and follow-up of the detailed exploitation plan and foster the development of individual exploitation plans by each partner during the project as the results mature. This task also includes the planning, organization, and participation in industry events.

Final architecture, traceability, and trustworthiness for complex experiment-driven analytics

This deliverable will report on the final architecture of the ExtremeXP framework, including: (a) Updates and improvements on the initial architecture documented in D2.1 and (b) the traceability and trustworthiness implemented using distributed ledger and smart contract methods for registering access to data and their contextual circumstances.

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.

Integrated ExtremeXP Framework

This deliverable will document the ExtremeXP framework, produced by a CI/CD approach to homogenize the results from WP3-5 into an integrated prototype. Best practices will be employed to set common methodologies, tools and processes for the development of the ExtremeXP framework core and support services and, consequently, will a) minimize incompatibilities and promote coherence within the different services; b) maintain a stable integrated software tree from which the different bundled releases can be more easily generated and retrieved; and c) manage the publication of the relevant ExtremeXP packages (software code) on a public repository of choice.

Core framework services – Monitoring, Planning, Scheduling

This deliverable will document the development of (a) the Adaptive Experiment Planning artifact of the ExtremeXP framework, i.e., the artifact that continuously determines which complex analytics variant should be executed, given a user intent, profile, and context as input; (b) the robust monitoring service and corresponding data abstraction layer of the framework, including adaptive monitoring techniques for low-footprint monitoring of the system, and in-situ data processing for streams of data; (c) the tools to optimally orchestrate an experiment workflow considering different techniques of virtualisation and heterogenous execution environments. It will include prototype libraries and/or tools and will document their implementation and usage information.

Visualization and explainability services

This deliverable will present the developed techniques for efficient, explorative and adaptive visualisation for different types of extreme data, including interactive and self-service visual analytics (e.g., visual Analytics-as-a-Service), lightweight, efficient in-memory indexes for visualization, models of visual interactions on experiment-based analytics and visual UIs which progressively adapt to the user visual operations and achieve low latency. Further, it will report on the developed explainability mechanisms for supporting the complete workflow of an experimentation-based analytics process, which will include: explainability on experimental process configuration options; user-role adaptable explainability mechanisms; and explanations that target to identify performance regressions. 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.

Human in the loop services – User intents, profiles, feedback

This deliverable will document the implemented mechanisms for (1) capturing the user intents, preferences, and constraints, and (2) mapping the intents to possible complex analytics workflows. Further, it will describe the implemented methods for learning user profiles through user interactions with the system and user feedback collected via gamification. It will include prototype libraries and/or tools and will document their implementation and usage information.

Constraint-aware ML and analytics services

This deliverable will present the implementation of new methods for (a) embedding constraining mechanisms in machine learning models and (2) efficiently selection algorithms and models (AutoML) given the dynamic needs. Further, it will present methods that adapt individual (constraint-aware) models and strategies for algorithm and model selection, in order to enable continuous updating (e.g., improvement of the decision making) as new data through experiences/simulations become available. It will include prototype libraries and/or tools and will document their implementation and usage information.

Pilot demonstrators and validation report

This deliverable will report on the design and execution of a validation plan with the contribution of all use cases. The plan will describe the testing and evaluation (user studies, controlled experiments) that will be performed to assess the impact of using the ExtremeXP framework to optimize complex analytics and visualizations for each use case. The deliverable will collect and consolidate the results from use-case validation performed and the outcomes will permit us to assess the scientific and technical contribution of the ExtremeXP project.

Publications

Distributed Data Security in Digital Health: Self-Sovereign Identity, Access Control, and Blockchain-based Log Records

Auteurs: Nicollas R. de Oliveira, Yago de R. dos Santos, Guilherme N. N. Barbosa, Lucio Henrik A. Reis, Ana Carolina R. Mendes, Marcela Tuler de Oliveira, Dianne S. V. de Medeiros, Diogo M. F. Mattos
Publié dans: 2024 6th International Conference on Blockchain Computing and Applications (BCCA), 2025
Éditeur: IEEE
DOI: 10.1109/BCCA62388.2024.10844453

Software Architecture for Data Intensive Systems Workshop

Auteurs: Ilias Gerostathopoulos et al.
Publié dans: First International Workshop on Software Architecture for Data-Intensive Systems (SADIS@ECSA 2025), 2025
Éditeur: First International Workshop on Software Architecture for Data-Intensive Systems (SADIS@ECSA 2025)

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

ExpEngine: A Tool for Data Analytics Workflow Optimization Through User-Driven Experimentation

Auteurs: Keerthiga Rajenthiram, Milad Abdullah, Ilias Gerostathopoulos, Petr Hnětynka, Tomáš Bureš
Publié dans: 2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), 2024
Éditeur: IEEE
DOI: 10.1109/ACSOS-C63493.2024.00058

Urban Flash Flood Prediction Through High Resolution Deep Learning Approach

Auteurs: P. Delporte, et al.
Publié dans: LPS 2025, Conference Proceedings., 2025
Éditeur: ESA Int.

ExperimentLens: Interactive Visual Analytics and Explainability for ML Experiment Management.

Auteurs: Maroulis, S., Stamatopoulos, V., Gidarakos, P., Tsopelas, K., Masouras, N., Kozanis, K., Theologitis, N., Papastefanatos, G., Giannopoulos, G., & Nilsson, E
Publié dans: VLDB 2025 Workshops – GuideAI Workshop., 2025
Éditeur: VLDB 2025 Workshops – GuideAI Workshop.

Urban Flash Flood Prediction through High-Resolution Hydrodynamic Modeling and Machine Learning Approaches

Auteurs: Kévin Larnier, Jacques Coves, Gwendoline Stéphan, Pauline Delporte, Loïc Dumas, Yasmine Boulfani
Publié dans: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 2024, ISBN 9798350361482
Éditeur: IEEE
DOI: 10.1109/IGARSS53475.2024.10641091

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

Partial Adaptive Indexing for Approximate Visual Analytics

Auteurs: Stavros Maroullis et al.
Publié dans: BigVis 2024, 2024
Éditeur: BigVis 2024

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

QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data

Auteurs: Giorgos Alexiou et al.
Publié dans: EDBT
Éditeur: OpenProceedings.org
DOI: 10.48786/EDBT.2025.10

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

GLOVES: Global Counterfactual based Visual Explanations

Auteurs: Panagiotis Gidarakos et al.
Publié dans: 2024
Éditeur: OpenProceedings.org
DOI: 10.48786/EDBT.2025.92

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

A Model-Based Approach to Experiment-Driven Evolution of ML Workflows

Auteurs: Petr Hnětynka, Tomáš Bureš, Ilias Gerostathopoulos, Milad Abdullah, Keerthiga Rajenthiram
Publié dans: Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering, 2025
Éditeur: SCITEPRESS - Science and Technology Publications
DOI: 10.5220/0013380500003896

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: ICDE, 2023
Éditeur: IEEE
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

Towards Continuous Experiment-Driven MLOps

Auteurs: Keerthiga Rajenthiram, Milad Abdullah, Ilias Gerostathopoulos, Petr Hnětynka, Tomáš Bureš, Gerard Pons, Besim Bilalli, Anna Queralt
Publié dans: 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN), 2025
Éditeur: IEEE
DOI: 10.1109/CAIN66642.2025.00018

A Synthesis of Green Architectural Tactics for ML-Enabled Systems

Auteurs: Heli Järvenpää; Patricia Lago; Justus Bogner; Grace A. Lewis; Henry Muccini; Ipek Ozkaya
Publié dans: Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society, 2024
Éditeur: ACM
DOI: 10.48550/ARXIV.2312.09610

Robin: A Systematic Literature Mapping Management Tool

Auteurs: Milad Abdullah, Michal Töpfer, Tomáš Bureš
Publié dans: 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2024
Éditeur: IEEE
DOI: 10.1109/SEAA64295.2024.00051

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

Performance Analysis of Distributed GPU-Accelerated Task-Based Workflows

Auteurs: Nogueira Lobo de Carvalho, Marcos; Queralt Calafat, Anna; Romero Moral, Óscar; Simitsis, Alkis; Tatu, Cristian; Badia Sala, Rosa Maria
Publié dans: instname:Universitat Politècnica de Catalunya (UPC), 2024
Éditeur: OpenProceedings.org
DOI: 10.48786/EDBT.2024.59

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

Surrogate Modelling and User‑In‑The‑Loop Experimentation for Urban Flood Prediction: The ExtremeXP Approach

Auteurs: P. Delporte, et al.
Publié dans: ESA Living Planet Symposium 2025, Milan, Italy, 2025
Éditeur: ESA Int

Interpreting Workflow Architectures by LLMs

Auteurs: Michal Töpfer, Tomáš Bureš, František Plášil, Petr Hnětynka
Publié dans: Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering, 2025
Éditeur: SCITEPRESS - Science and Technology Publications
DOI: 10.5220/0013358000003928

Extending UEBA for Emerging Threats

Auteurs: Carolina Ferández et al.
Publié dans: 2024
Éditeur: GEANT Security Days
DOI: 10.2760/46796

Tutoring LLM into a Better CUDA Optimizer

Auteurs: Matyáš Brabec; Jiří Klepl; Michal Töpfer; Martin Kruliš
Publié dans: Lecture Notes in Computer Science ISBN: 9783031998560, 2025
Éditeur: springer Nature
DOI: 10.48550/ARXIV.2510.16933

Experiment Cards: A Documentation Framework for Trustworthy AI Experiments

Auteurs: Georgia Gkioka, Dimitris Apostolou, Yiannis Verginadis, Gregoris Mentzas
Publié dans: 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI), 2025
Éditeur: IEEE
DOI: 10.1109/ICTAI66417.2025.00065

A Combined Approach to Performance Regression Testing Resource Usage Reduction

Auteurs: Milad Abdullah, David Georg Reichelt, Vojtěch Horký, Lubomír Bulej, Tomáš Bureš, Petr Tůma
Publié dans: Proceedings of the 21st International Conference on Predictive Models and Data Analytics in Software Engineering, 2025
Éditeur: ACM
DOI: 10.1145/3727582.3728690

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

LLM-PQA: LLM-enhanced Prediction Query Answering

Auteurs: Ziyu Li; Wenjie Zhao; Asterios Katsifodimos; Rihan Hai 0001
Publié dans: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024
Éditeur: ACM
DOI: 10.48550/ARXIV.2409.01140

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-Based Semantic Segmentation

Auteurs: Niharika Hegde; Shishir Muralidhara; René Schuster; Didier Stricker
Publié dans: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025
Éditeur: IEEE
DOI: 10.48550/ARXIV.2411.17610

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

An architectural perspective on MLOps: Structures, processes, tools, and stakeholders

Auteurs: Faezeh Amou Najafabadi, Justus Bogner, Ilias Gerostathopoulos, Patricia Lago
Publié dans: Information and Software Technology, Numéro 193, 2026, ISSN 0950-5849
Éditeur: Elsevier BV
DOI: 10.1016/J.INFSOF.2026.108029

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

RBD24 : A labelled dataset with risk activities using log application data

Auteurs: Albert Calvo, Santiago Escuder, Nil Ortiz, Josep Escrig, Maxime Compastié
Publié dans: Computers & Security, Numéro 150, 2025, ISSN 0167-4048
Éditeur: Elsevier BV
DOI: 10.1016/J.COSE.2024.104290

NFT-based Data Provenance for AI Transparency in Enterprise Information Systems

Auteurs: Yiannis Verginadis, Orestis Almpanoudis, Dimitris Apostolou, Marcela T. de Oliveira, Gregoris Mentzas
Publié dans: Procedia Computer Science, Numéro 256, 2025, ISSN 1877-0509
Éditeur: Elsevier BV
DOI: 10.1016/J.PROCS.2025.02.153

Reproducible experiments for generating pre-processing pipelines for AutoETL

Auteurs: Joseph Giovanelli, Besim Bilalli, Alberto Abelló, Fernando Silva-Coira, Guillermo de Bernardo
Publié dans: Information Systems, Numéro 120, 2025, ISSN 0306-4379
Éditeur: Elsevier BV
DOI: 10.1016/J.IS.2023.102314

GLOVES 2.0: Global Counterfactual-Based Visual Explanations

Auteurs: Nikolas Theologitis, Panagiotis Gidarakos, Stavros Maroulis, Loukas Kavouras, Giorgos Giannopoulos, George Papastefanatos
Publié dans: Lecture Notes in Computer Science, Software Architecture. ECSA 2025 Tracks and Workshops, 2025
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-032-04403-7_28

Reference Architecture of MLOps Workflows

Auteurs: Faezeh Amou Najafabadi
Publié dans: Lecture Notes in Computer Science, Software Architecture. ECSA 2024 Tracks and Workshops, 2024
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-71246-3_6

Accelerating Entity Resolution Through Vectorized Meta-blocking on GPUs

Auteurs: Nikolas Stamatopoulos, Vassilis Stamatopoulos, Giorgos Alexiou, Giorgos Giannopoulos, George Papastefanatos
Publié dans: Communications in Computer and Information Science, New Trends in Database and Information Systems, 2025
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-032-05727-3_14

Temporal Harmonization of Heterogeneous Software Logs: A Unified Model for Time-Series Analysis

Auteurs: Milad Abdullah, Petr Hnětynka, Jiyan Salim Mahmud
Publié dans: Lecture Notes in Computer Science, Software Architecture. ECSA 2025 Tracks and Workshops, 2025
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-032-04403-7_29

AutoML: A Tertiary Study of Phases, Methods, Tools, and Frameworks

Auteurs: Keerthiga Rajenthiram, Pauline Delporte, Patricia Lago
Publié dans: Lecture Notes in Computer Science, Software Architecture. ECSA 2025 Tracks and Workshops, 2025
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-032-04403-7_26

Explainable AI for cybersecurity decisions: challenges and opportunities

Auteurs: Albert Calvo, Sonu Preetam, Maxime Compastié
Publié dans: Explainable AI for Communications and Networking, 2025
Éditeur: Elsevier
DOI: 10.1016/B978-0-44-329135-7.00018-7

An Analysis of MLOps Architectures: A Systematic Mapping Study

Auteurs: Faezeh Amou Najafabadi, Justus Bogner, Ilias Gerostathopoulos, Patricia Lago
Publié dans: Lecture Notes in Computer Science, Software Architecture, 2024
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-70797-1_5

TimeVizBench: An Interactive Platform for Evaluating Techniques for Efficient Large Time Series Visualization

Auteurs: Vassilis Stamatopoulos, Stavros Maroulis, Christos Pantoleon, George Papastefanatos, Panos Vassiliadis
Publié dans: Communications in Computer and Information Science, New Trends in Database and Information Systems, 2025
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-032-05727-3_8

Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation

Auteurs: Gerard Pons, Besim Bilalli, Anna Queralt
Publié dans: Lecture Notes in Computer Science, The Semantic Web – ISWC 2024, 2024
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-77844-5_9

Evaluating Robustness of Machine Learning Models against Adversarial Attacks: Techniques, Countermeasures, and Performance Analysis

Auteurs: Nil Ortiz, Albert Calvo
Publié dans: JNIC
Éditeur: Lenguajes y Sistemas Informáticos

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