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Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Fernando Silva
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 janeiro 2009
005
Publicações

2023

Jay: A software framework for prototyping and evaluating offloading applications in hybrid edge clouds

Autores
Silva, J; Marques, ERB; Lopes, LMB; Silva, FMA;

Publicação
SOFTWARE-PRACTICE & EXPERIENCE

Abstract
We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

2023

MHVG2MTS: Multilayer Horizontal Visibility Graphs for Multivariate Time Series Analysis

Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, FMA;

Publicação
CoRR

Abstract

2022

Network Science - 7th International Winter Conference, NetSci-X 2022, Porto, Portugal, February 8-11, 2022, Proceedings

Autores
Ribeiro, P; Silva, F; Ferreira Mendes, JF; Laureano, RD;

Publicação
NetSci-X

Abstract

2022

Preface

Autores
Ribeiro, P; Silva, F; Mendes, JF; Laureano, R;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2022

Novel features for time series analysis: a complex networks approach

Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, F;

Publicação
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.

Teses
supervisionadas

2021

Uma solução de Business Intelligence para a área académica da U. Porto

Autor
André lage Sobral

Instituição
UP-FCUP

2021

Academic Management Information Interoperability Platform for Higher Education Institutions

Autor
Lucas Carvalho de Paula

Instituição
UP-FCUP

2021

A Predictive Analysis of Academic Success at Universidade do Porto

Autor
Rafael António Belokurows

Instituição
UP-FCUP

2021

Beyond Distributed Transactions through Exactly-once Exchanges

Autor
Ziad Ali Kassam

Instituição
UM

2021

Adaptive Computation Offloading in Mobile Edge Clouds

Autor
Joaquim Magalhães Esteves da Silva

Instituição
UP-FCUP