Ponències/Comunicacions de congressos
Recent Submissions
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Finding relevant information in big datasets with ML
(OpenProceedings, 2024)
Conference lecture
Open AccessDue to the abundance of data, noisy, irrelevant, or redundant features often need to be identified and discarded. Feature selection is a collection of methods used to ensure that only relevant data are used for a data ... -
GLiDE: Integrated Gamified Learning Dashboard Environment
(CEUR-WS.org, 2024)
Conference lecture
Open AccessThe Integrated Gamified Learning Dashboard Environment (GLiDE) project aims at fostering student engagement, teamwork, and project performance in software engineering education. By integrating gamification elements and ... -
RE-Miner: Mining mobile user reviews with feature extraction and emotion classification
(CEUR-WS.org, 2024)
Conference report
Open AccessIn the context of app stores, user reviews are pivotal on supporting multiple requirements engineering tasks. Among these, feature extraction and emotion classification play a crucial role in requirements prioritization, ... -
There is no data science without data governance: a proposal based on knowledge graphs
(CEUR-WS.org, 2024)
Conference lecture
Open AccessData Science and data-driven Artificial Intelligence are here to stay and they are expected to further transform the current global economy. From a technical point of view, there is an overall agreement that disciplines ... -
A data-science pipeline to enable the interpretability of many-objective feature selection
(CEUR-WS.org, 2024)
Conference lecture
Open AccessMany-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, MOFS typically returns a large set of ... -
Discovery of semantic non-syntactic joins
(CEUR-WS.org, 2024)
Conference lecture
Open AccessData discovery is an essential step in the data integration pipeline involving finding datasets whose combined information provides relevant insights. Discovering joinable attributes requires assessing the closeness of the ... -
HealthMesh: An architectural framework for federated healthcare data management
(CEUR-WS.org, 2024)
Conference report
Open AccessRecently, significant milestones have been achieved in the field of healthcare data analysis. However, alongside these accomplishments, substantial data-related challenges have emerged in the domain of big data management. ... -
Unveiling competition dynamics in mobile app markets through user reviews
(Springer, 2024)
Conference report
Restricted access - publisher's policy[Context and motivation] User reviews published in mobile app repositories are essential for understanding user satisfaction and engagement within a specific market segment. [Question/problem] Manual analysis of reviews ... -
Performance analysis of distributed GPU-accelerated task-based workflows
(OpenProceedings, 2024)
Conference report
Open AccessWe present an empirical approach to identify the key factors affecting the execution performance of task-based workflows on a High Performance Computing (HPC) infrastructure composed of heterogeneous CPU-GPU clusters. Our ... -
Do DL models and training environments have an impact on energy consumption?
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Conference report
Open AccessCurrent research in the computer vision field mainly focuses on improving Deep Learning (DL) correctness and inference time performance. However, there is still little work on the huge carbon footprint that has training ... -
Adaptive task-oriented chatbots using feature-based knowledge bases
(Springer, 2023)
Conference lecture
Open AccessTask-oriented chatbots relying on a knowledge base for domain-specific content exploitation have been largely addressed in research and industry applications. Despite this, multiple challenges remain to be fully conquered, ... -
Comparision of models built using AutoML and data fusion
(Springer, 2022)
Conference report
Open AccessAutomated machine learning (AutoML) has made life easier for data analysts or scientists by providing quick insights into data by building machine learning (ML) models. AutoML techniques are applied to vast areas from image ...
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