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Sobre

Sobre

Sou investigadora no Laboratório de Software Confiável (HASLab) e Professora Auxiliar Convidada na Universidade do Minho, onde lecciono nas disciplinas de Tolerância a Faltas, Sistemas Distribuídos e Sistemas Operativos. Os meus interesses de investigação incluem sistemas confiáveis de gestão e processamento de dados e diferentes mecanismos de tolerância a faltas.

Participei em múltiplos projetos de investigação Nacionais, Europeus e de inovação em colaboração com empresas da área da Engenharia de Software, dos Seguros e da Energia.

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

TADA: A Toolkit for Approximate Distributed Agreement

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

Publicação
DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2023

Abstract
Approximate agreement has long been relegated to the sidelines compared to exact consensus, with its most notable application being clock synchronisation. Other proposed applications stemming from control theory target multi-agent consensus, namely for sensor stabilisation, coordination in robotics, and trust estimation. Several proposals for approximate agreement follow the Mean Subsequence Reduce approach, simply applying different functions at each phase. However, taking clock synchronisation as an example, applications do not fit neatly into the MSR model: Instead they require adapting the algorithms' internals. Our contribution is two-fold. First, we identify additional configuration points, establishing a more general template of MSR approximate agreement algorithms. We then show how this allows us to implement not only generic algorithms but also those tailored for specific purposes (clock synchronisation). Second, we propose a toolkit for making approximate agreement practical, providing classical implementations as well as allow these to be configured for specific purposes. We validate the implementation with classical algorithms and clock synchronisation.

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.

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.

2022

A Blockchain-based Data Market for Renewable Energy Forecasts

Autores
Coelho, F; Silva, F; Goncalves, C; Bessa, R; Alonso, A;

Publicação
2022 FOURTH INTERNATIONAL CONFERENCE ON BLOCKCHAIN COMPUTING AND APPLICATIONS (BCCA)

Abstract
This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

Teses
supervisionadas

2023

Distributed query processing in dataspaces

Autor
João Pedro Araújo Parente

Instituição

2022

A toolkit for approximate consensus

Autor
Eduardo Lourenço da Conceição

Instituição

2022

Gestão de permissões e acesso a dados para Hyperledger Fabric

Autor
João Pedro Araújo Parente

Instituição

2022

Interpretação e execução de SQL sobre ficheiros

Autor
Bruno Filipe de Sousa Dias

Instituição

2022

DriftWood: Consenso descentralizado

Autor
André da Silva Gonçalves

Instituição