Publicacoes - INESC TEC

Publicações

Publicações por Vítor Santos Costa

1998

Distance: A New Metric for Controlling Granularity for Parallel Execution

Autores
Shen, K; Costa, VS; King, A;

Publicação
Proceedings of the 1998 Joint International Conference and Symposium on Logic Programming, Manchester, UK, June 15-19, 1998

Abstract

1992

Generalized Stack-copying for And-Or Parallel Execution of Full Prolog

Autores
Gupta, G; Hermenegildo, MV; Costa, VS;

Publicação
Workshop on Concurrent and Parallel Implementations (sessions A and B), held at IJCSLP'92, Washington, DC, USA, November 1992

Abstract

1997

Evaluating the impact of coherence protocols on parallel logic programming systems

Autores
Costa, VS; Bianchini, R; Dutra, IdC;

Publicação
Fifth Euromicro Workshop on Parallel and Distributed Processing (PDP '97), January 22-24, 1997, University of Westminster, London, UK

Abstract

1999

The BEAM: A first EAM Implementation

Autores
Lopes, R; Costa, VS;

Publicação
1999 Joint Conference on Declarative Programming, AGP'99, L'Aquila, Italy, September 6-9, 1999

Abstract

1999

Distance: A New Metric for Controlling Granularity for Parallel Execution

Autores
Shen, K; Costa, VS; King, A;

Publicação
Journal of Functional and Logic Programming

Abstract

2005

View learning for statistical relational learning: With an application to mammography

Autores
Davis, J; Burnside, E; Dutra, I; Page, D; Ramakrishnan, R; Costa, VS; Shavlik, J;

Publicação
IJCAI International Joint Conference on Artificial Intelligence

Abstract
Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in the Bayes net sense) connecting entries in different rows of a relational table, or in different tables. Nevertheless, SRL approaches currently are constrained to use the existing database schema. For many database applications, users find it profitable to define alternative "views" of the database, in effect defining new fields or tables. Such new fields or tables can also be highly useful in learning. We provide SRL with the capability of learning new views.

FecharLer Abstract