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Sobre

Sobre

Colaborei no projecto Europeu GORDA, cujo objectivo era fomentar a utilização de replicação de base de dado como forma de resolver os desafios encontrados nos sistemas de base de dados actuais. O meu trabalho foi de dotar o projecto com uma consola gráfica de monitorização e gestão utilizando JMX.

Também estive envolvida no projecto P-SON, em particular no desenvolvimento de aplicações para o protocolo de disseminação epidémica NeEM. Neste contexto, trabalhei numa arquitectura de caching e disseminação de feeds RSS que utiliza redes P2P para este fim e, posteriormente, numa arquitectura genérica content-push baseada em feeds Web e serviços e redes sociais, no qual se baseou a minha dissertação de Mestrado. Também trabalhei num protocolo e disseminação epidémica que tira partido dos interesses partilhados dos participantes sem chegar a revelá-los.

No contexto do meu Doutoramento, o meu trabalho focou-se na forma de aproximar as aplicações tradicionais centradas numa base de dados e as promessas de alta disponibilidade.

Actualmente, faço parte do projecto europeu CloudDBAppliance e tenho estado, também, a colaborar com o CPES no contexto dos projectos europeus UPGRID e InteGrid.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ana Nunes Alonso
  • Cargo

    Investigador Auxiliar
  • Desde

    01 fevereiro 2012
006
Publicações

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

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

TADA: A Toolkit for Approximate Distributed Agreement

Autores
da Conceição, EL; Nunes Alonso, A; Oliveira, RC; Pereira, JO;

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

Abstract

2023

TADA: A Toolkit for Approximate Distributed Agreement

Autores
da Conceição, EL; Alonso, AN; Oliveira, RC; Pereira, JO;

Publicação
Distributed Applications and Interoperable Systems - 23rd IFIP WG 6.1 International Conference, DAIS 2023, Held as Part of the 18th International Federated Conference on Distributed Computing Techniques, DisCoTec 2023, Lisbon, Portugal, June 19-23, 2023, Proceedings

Abstract

2023

TiQuE: Improving the Transactional Performance of Analytical Systems for True HybridWorkloads

Autores
Faria, N; Pereira, J; Alonso, AN; Vilaca, R; Koning, Y; Nes, N;

Publicação
PROCEEDINGS OF THE VLDB ENDOWMENT

Abstract
Transactions have been a key issue in database management for a long time and there are a plethora of architectures and algorithms to support and implement them. The current state-of-the-art is focused on storage management and is tightly coupled with its design, leading, for instance, to the need for completely new engines to support new features such as Hybrid Transactional Analytical Processing (HTAP). We address this challenge with a proposal to implement transactional logic in a query language such as SQL. This means that our approach can be layered on existing analytical systems but that the retrieval of a transactional snapshot and the validation of update transactions runs in the server and can take advantage of advanced query execution capabilities of an optimizing query engine. We demonstrate our proposal, TiQuE, on MonetDB and obtain an average 500x improvement in transactional throughput while retaining good performance on analytical queries, making it competitive with the state-of-the-art HTAP systems.

Teses
supervisionadas

2022

A toolkit for approximate consensus

Autor
Eduardo Lourenço da Conceição

Instituição
UM

2022

Interpretação e execução de SQL sobre ficheiros

Autor
Bruno Filipe de Sousa Dias

Instituição
UM

2022

DriftWood: Consenso descentralizado

Autor
André da Silva Gonçalves

Instituição
UM

2022

Gestão de permissões e acesso a dados para Hyperledger Fabric

Autor
João Pedro Araújo Parente

Instituição
UM

2021

Acordo Distribuído Aproximado

Autor
Joaquim Manuel Gonçalves Oliveira

Instituição
UM