Detalhes
Nome
Pedro Manuel RibeiroCargo
Investigador SéniorDesde
03 maio 2010
Nacionalidade
PortugalCentro
Centro de Sistemas de Computação AvançadaContactos
+351220402963
pedro.p.ribeiro@inesctec.pt
2024
Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, F;
Publicação
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
Abstract
In recent years, there has been a surge in the prevalence of high- and multidimensional temporal data across various scientific disciplines. These datasets are characterized by their vast size and challenging potential for analysis. Such data typically exhibit serial and cross-dependency and possess high dimensionality, thereby introducing additional complexities to conventional time series analysis methods. To address these challenges, a recent and complementary approach has emerged, known as network-based analysis methods for multivariate time series. In univariate settings, quantile graphs have been employed to capture temporal transition properties and reduce data dimensionality by mapping observations to a smaller set of sample quantiles. To confront the increasingly prominent issue of high dimensionality, we propose an extension of quantile graphs into a multivariate variant, which we term Multilayer Quantile Graphs. In this innovative mapping, each time series is transformed into a quantile graph, and inter-layer connections are established to link contemporaneous quantiles of pairwise series. This enables the analysis of dynamic transitions across multiple dimensions. In this study, we demonstrate the effectiveness of this new mapping using synthetic and benchmark multivariate time series datasets. We delve into the resulting network's topological structures, extract network features, and employ these features for original dataset analysis. Furthermore, we compare our results with a recent method from the literature. The resulting multilayer network offers a significant reduction in the dimensionality of the original data while capturing serial and cross-dimensional transitions. This approach facilitates the characterization and analysis of large multivariate time series datasets through network analysis techniques.
2023
Autores
Barbosa, A; Ribeiro, P; Dutra, I;
Publicação
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2
Abstract
Association Football is probably the world's most popular sport. Being able to characterise and compare football players is therefore a very important and impactful task. In this work we introduce spatial flow motifs as an extension of previous work on this problem, by incorporating both temporal and spatial information into the network analysis of football data. Our approach considers passing sequences and the role of the player in those sequences, complemented with the physical position of the field where the passes occurred. We provide experimental results of our proposed methodology on real-life event data from the Italian League, showing we can more accurately identify players when compared to using purely topological data.
2023
Autores
Ferreira, J; Barbosa, A; Ribeiro, P;
Publicação
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2
Abstract
Many complex systems exist in the physical world and therefore can be modeled by networks in which their nodes and edges are embedded in space. However, classical network motifs only use purely topological information and disregard other features. In this paper we introduce a novel and general subgraph abstraction that incorporates spatial information, therefore enriching its characterization power. Moreover, we describe and implement a method to compute and count our spatial subgraphs in any given network. We also provide initial experimental results by using our methodology to produce spatial fingerprints of real road networks, showcasing its discrimination power and how it captures more than just simple topology.
2023
Autores
Oliveira, HS; Ribeiro, PP; Oliveira, HP;
Publicação
Pattern Recognition and Image Analysis - 11th Iberian Conference, IbPRIA 2023, Alicante, Spain, June 27-30, 2023, Proceedings
Abstract
2023
Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, FMA;
Publicação
CoRR
Abstract
Teses supervisionadas
2022
Autor
Maria Hermínia Esteves de Carvalho
Instituição
UP-FCUP
2022
Autor
William Tostes Lobo
Instituição
UP-FEUP
2022
Autor
Jongmin Han
Instituição
UP-FEUP
2021
Autor
António Pedro de Novais da Cruz
Instituição
UP-FEUP
2021
Autor
Nuno Miguel Rodrigues Gomes
Instituição
UP-FEUP
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