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Sobre

Sobre

O Fábio nasceu em Lisboa, Portugal em 1988. Licenciou-se em Engenharia de Redes de Computadores e Multimédia em 2011 pelo Instituto Superior de Engenharia de Lisboa. Decidiu depois prosseguir os seus estudos, tendo ingressado no Mestrado em Engenharia Informática da Universidade do Minho, de onde obteve o grau de Mestre em 2013. Desde essa altura, o Fábio é invetigador no HASLab, Laboratório Associado do INESC TEC. Doutorou-se em 2018 no programa doutoral em informática MAP-i administrado em co-tutela pelas  Universidades do Minho, Aveiro e Porto. Conjuntamente, o seu trabalho de investigação e tese de doutoramento focam-se em ferramentas de "Data Analytics" para sistemas de larga escala, vulgo "BigData". De entre outros tópicos, o Fábio interessa-se também por sistemas de "Benchmarking" e por sistemas de processamento transacional distribuídos. Nos seus tempos livres, gosta de viajar e de fotografia.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Fábio André Coelho
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2014
009
Publicações

2024

Databases in Edge and Fog Environments : A Survey

Autores
Meruje Ferreira, LM; Coelho, F; Pereira, J;

Publicação
ACM Computing Surveys

Abstract
While a significant number of databases are deployed in cloud environments, pushing part or all data storage and querying planes closer to their sources (i.e., to the edge) can provide advantages in latency, connectivity, privacy, energy and scalability. This article dissects the advantages provided by databases in edge and fog environments, by surveying application domains and discussing the key drivers for pushing database systems to the edge. At the same time, it also identifies the main challenges faced by developers in this new environment, and analysis the mechanisms employed to deal with them. By providing an overview of the current state of edge and fog databases, this survey provides valuable insights into future research directions.

2023

Privacy-Preserving Machine Learning in Life Insurance Risk Prediction

Autores
Pereira, K; Vinagre, J; Alonso, AN; Coelho, F; Carvalho, M;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models.

2023

Analysis of Flexibility-centric Energy and Cross-sector Business Models

Autores
Rodrigues, L; Faria, D; Coelho, F; Mello, J; Saraiva, JT; Villar, J; Bessa, RJ;

Publicação
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

2023

Towards MRAM Byte-Addressable Persistent Memory in Edge Database Systems

Autores
Ferreira, LM; Coelho, F; Pereira, JO;

Publicação
Joint Proceedings of Workshops at the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, August 28 - September 1, 2023.

Abstract
There is a growing demand for persistent data in IoT, edge and similar resource-constrained devices. However, standard FLASH memory-based solutions present performance, energy, and reliability limitations in these applications. We propose MRAM persistent memory as an alternative to FLASH based storage. Preliminary experimental results show that its performance, power consumption, and reliability in typical database workloads is competitive for resource-constrained devices. This opens up new opportunities, as well as challenges, for small-scale database systems. MRAM is tested for its raw performance and applicability to key-value and relational database systems on resource-constrained devices. Improvements of as much as three orders of magnitude in write performance for key-value systems were observed in comparison to an alternative NAND FLASH based device. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

2023

LOOM: A Closed-Box Disaggregated Database System

Autores
Coelho, F; Alonso, AN; Ferreira, L; Pereira, J; Oliveira, R;

Publicação
PROCEEDINGS OF12TH LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE AND SECURE COMPUTING, LADC 2023

Abstract
Cloud native database systems provide highly available and scalable services as part of cloud platforms by transparently replicating and partitioning data across automatically managed resources. Some systems, such as Google Spanner, are designed and implemented from scratch. Others, such as Amazon Aurora, derive from traditional database systems for better compatibility but disaggregate storage to cloud services. Unfortunately, because they follow an open-box approach and fork the original code base, they are difficult to implement and maintain. We address this problem with Loom, a replicated and partitioned database system built on top of PostgreSQL that delegates durable storage to a distributed log native to the cloud. Unlike previous disaggregation proposals, Loom is a closed-box approach that uses the original server through existing interfaces to simplify implementation and improve robustness and maintainability. Experimental evaluation achieves 6x higher throughput and 5x lower response time than standard replication and competes with the state of the art in cloud and HPC hardware.

Teses
supervisionadas

2018

iPortalDoc  - Motor de Workflow

Autor
Sérgio André Oliveira de Lima Moreira

Instituição
UP-FEUP

2016

Sistema de bancada laboratorial para tarefas repetitivas.

Autor
Cláudia Daniela Costa Rocha

Instituição
UP-FEUP

2016

An Android GUI Crawler for Test Case Generation and Runtime Analysis

Autor
Miguel Bruno Rego Freitas

Instituição
UP-FEUP

2016

Desenvolvimento de um protótipo de um simulador de bloqueio do plexo braquial

Autor
Stéphanie Coelho Monteiro

Instituição
IPB

2016

Sistema Inteligente de Gestão de Energia Elétrica - Automação de uma habitação

Autor
Pedro Gonçalo Guedes Lopes Praça

Instituição
IPB