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Detalhes

Detalhes

  • Nome

    Inês Dutra
  • Cluster

    Informática
  • Cargo

    Investigador Colaborador Externo
  • Desde

    01 janeiro 2009
005
Publicações

2018

atSNPInfrastructure, a Case Study for Searching Billions of Records While Providing Significant Cost Savings over Cloud Providers

Autores
Harrison, C; Keles, S; Hudson, R; Shin, S; Dutra, I;

Publicação
2018 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPS Workshops 2018, Vancouver, BC, Canada, May 21-25, 2018

Abstract
We explore the feasibility of a database storage engine housing up to 307 billion genetic Single Nucleotide Polymorphisms (SNP) for online access. We evaluate database storage engines and implement a solution utilizing factors such as dataset size, information gain, cost and hardware constraints. Our solution provides a full feature functional model for scalable storage and query-ability for researchers exploring the SNP's in the human genome. We address the scalability problem by building physical infrastructure and comparing final costs to a major cloud provider. © 2018 IEEE.

2018

Improving Candidate Quality of Probabilistic Logic Models

Autores
Real, JC; Dries, A; Dutra, I; Rocha, R;

Publicação
Technical Communications of the 34th International Conference on Logic Programming, ICLP 2018, July 14-17, 2018, Oxford, United Kingdom

Abstract

2017

Managing Diabetes: Counselling Supported by User Data in a Mobile Platform

Autores
Machado, D; Dutra, I; Brandão, P; Costa, VS;

Publicação
Proceedings of the Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR 2017 hosted by International Joint Conference on Rules and Reasoning 2017 (RuleML+RR 2017), London, UK, July 11-15, 2017.

Abstract
Diabetes management is a complex problem. The patient needs to monitor several parameters in order to react in the most appropriate way. Different situations require the diabetic to understand and evaluate different rules. The main source of knowledge for those rules arises from medical practice and is usually transmitted through medical appointments. Given this initial advice, most patient are on a continuous process of managing the disease, toward achieving the best possible quality of life. Motivated by recent aadvances in diabetes monitoring devices, we introduce a diabetes support system designed to accompany the user, advising her and providing early guidance to avoid some of the many complications associated with diabetes. To accomplish this goal, we incorporate standard medical protocols, advice and directives in a Rule Based System (RBS). This RBS which we call Advice Rule Based System (ARBS) is capable of advising and uncovering possible causes for different occurrences. We believe that this solution is not only beneficial to the patient, but may also may be of use to the clinitians advising the patient. The device has continuous contact with the patient, thus it can provide early response if/where needed, Moreover, the system can provide useful data, that an authorized medical expert can use while prescribing a particular treatment, or even when investingating this health problem. We have started to add data-mining algorithms and methods, to uncover hidden behavioural patterns that may lead to crisis situations. Ultimately, through refining the rule systems base don human and machine learning, our approach has the potential for personalising the system according to the habits and phenotype of its user. The system is to be incorporated in a currently developed diabetes management application for Android.

2017

Managing diabetes: Pattern discovery and counselling supported by user data in a mobile platform

Autores
Machado, D; Paiva, T; Dutra, I; Costa, VS; Brandao, P;

Publicação
2017 IEEE Symposium on Computers and Communications, ISCC 2017, Heraklion, Greece, July 3-6, 2017

Abstract
Diabetes management is a complex and a sensible problem as each diabetic is a unique case with particular needs. The optimal solution would be a constant monitoring of the diabetic's values and automatically acting accordingly. We propose an approach that guides the user and analyses the data gathered to give individual advice. By using data mining algorithms and methods, we uncover hidden behaviour patterns that may lead to crisis situations. These patterns can then be transformed into logical rules, able to trigger in a particular context, and advise the user. We believe that this solution, is not only beneficial for the diabetic, but also for the doctor accompanying the situation. The advice and rules are useful input that the medical expert can use while prescribing a particular treatment. During the data gathering phase, when the number of records is not enough to attain useful conclusions, a base set of logical rules, defined from medical protocols, directives and/or advice, is responsible for advise and guiding the user. The proposed system will accompany the user at start with generic advice, and with constant learning, advise the user more specifically. We discuss this approach describing the architecture of the system, its base rules and data mining component. The system is to be incorporated in a currently developed diabetes management application for Android. © 2017 IEEE.

2017

Estimation-based search space traversal in PILP environments

Autores
Côrte Real, J; Dutra, I; Rocha, R;

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

Abstract
Probabilistic Inductive Logic Programming (PILP) systems extend ILP by allowing the world to be represented using probabilistic facts and rules, and by learning probabilistic theories that can be used to make predictions. However, such systems can be inefficient both due to the large search space inherited from the ILP algorithm and to the probabilistic evaluation needed whenever a new candidate theory is generated. To address the latter issue, this work introduces probability estimators aimed at improving the efficiency of PILP systems. An estimator can avoid the computational cost of probabilistic theory evaluation by providing an estimate of the value of the combination of two subtheories. Experiments are performed on three real-world datasets of different areas (biology, medical and web-based) and show that, by reducing the number of theories to be evaluated, the estimators can significantly shorten the execution time without losing probabilistic accuracy. © Springer International Publishing AG 2017.

Teses
supervisionadas

2017

Execução e Gestão de Aplicações Conteinerizadas

Autor
Diogo Cristiano dos Santos Reis

Instituição
UP-FCUP

2017

Weighted Multiple Kernel Learning for Breast Cancer Diagnosis applied to Mammograms

Autor
Tiago André Guedes Santos

Instituição
UP-FCUP

2017

Improving the search for multi-relational concepts in ILP

Autor
Alberto José Rajão Barbosa

Instituição
UP-FCUP

2016

Implementação e Avaliação do Algoritmo MCTS-UCT para o jogo Chinese Checkers

Autor
Jhonny Manuel Campos Moreira

Instituição
UP-FCUP

2016

Melhorias no controlo da glicemia através de data mining

Autor
Tiago Alexandre Costa Paiva

Instituição
UP-FCUP