Experimental evaluation of a caching technique

Detalhes bibliográficos
Autor(a) principal: Nuno Fonseca
Data de Publicação: 2003
Outros Autores: Vítor Santos Costa, Fernando Silva, Rui Camacho
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/67438
Resumo: Inductive Logic Programming (ILP) is a Machine Learning technique that has been quite successful in knowledge discovery for relational domains. ILP systems implemented in Prolog challenge the limits of Prolog systems due to heavy usage of resources such as database accesses and memory usage, and to very long execution times. The major reason to implement ILP systems in Prolog is that the inference mechanism implemented by the Prolog engine is fundamental to most ILP learning algorithms. ILP systems can therefore benefit from the extensive performance improvement work that has taken place for Prolog. On the other hand, ILP is a non-classical Prolog application because it uses large sets of ground facts and requires storing a large search tree. One major criticism of ILP systems is that they often have long running times. A technique that tries to tackle this problem is coverage caching [?]. Coverage caching stores previous results in order to avoid recomputation. Naturally, this technique uses the Prolog internal database to store results. The question is: does coverage caching successfully reduce the ILP systems running time? To obtain an answer to this question we evaluated the impact of the coverage caching technique using the April [?] ILP system with the YAP Prolog system. To understand the results obtained we profiled Aprils execution and present initial results. The contribution of this paper is twofold: to an ILP researcher it provides an evaluation of the coverage caching technique implemented in Prolog using well known datasets; to a Prolog implementation researcher it shows the need of efficient internal database indexing mechanisms.
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spelling Experimental evaluation of a caching techniqueEngenharia de computadores, Engenharia electrotécnica, electrónica e informáticaComputer engineering, Electrical engineering, Electronic engineering, Information engineeringInductive Logic Programming (ILP) is a Machine Learning technique that has been quite successful in knowledge discovery for relational domains. ILP systems implemented in Prolog challenge the limits of Prolog systems due to heavy usage of resources such as database accesses and memory usage, and to very long execution times. The major reason to implement ILP systems in Prolog is that the inference mechanism implemented by the Prolog engine is fundamental to most ILP learning algorithms. ILP systems can therefore benefit from the extensive performance improvement work that has taken place for Prolog. On the other hand, ILP is a non-classical Prolog application because it uses large sets of ground facts and requires storing a large search tree. One major criticism of ILP systems is that they often have long running times. A technique that tries to tackle this problem is coverage caching [?]. Coverage caching stores previous results in order to avoid recomputation. Naturally, this technique uses the Prolog internal database to store results. The question is: does coverage caching successfully reduce the ILP systems running time? To obtain an answer to this question we evaluated the impact of the coverage caching technique using the April [?] ILP system with the YAP Prolog system. To understand the results obtained we profiled Aprils execution and present initial results. The contribution of this paper is twofold: to an ILP researcher it provides an evaluation of the coverage caching technique implemented in Prolog using well known datasets; to a Prolog implementation researcher it shows the need of efficient internal database indexing mechanisms.20032003-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/67438engNuno FonsecaVítor Santos CostaFernando SilvaRui Camachoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:22:24Zoai:repositorio-aberto.up.pt:10216/67438Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:21:59.753190Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Experimental evaluation of a caching technique
title Experimental evaluation of a caching technique
spellingShingle Experimental evaluation of a caching technique
Nuno Fonseca
Engenharia de computadores, Engenharia electrotécnica, electrónica e informática
Computer engineering, Electrical engineering, Electronic engineering, Information engineering
title_short Experimental evaluation of a caching technique
title_full Experimental evaluation of a caching technique
title_fullStr Experimental evaluation of a caching technique
title_full_unstemmed Experimental evaluation of a caching technique
title_sort Experimental evaluation of a caching technique
author Nuno Fonseca
author_facet Nuno Fonseca
Vítor Santos Costa
Fernando Silva
Rui Camacho
author_role author
author2 Vítor Santos Costa
Fernando Silva
Rui Camacho
author2_role author
author
author
dc.contributor.author.fl_str_mv Nuno Fonseca
Vítor Santos Costa
Fernando Silva
Rui Camacho
dc.subject.por.fl_str_mv Engenharia de computadores, Engenharia electrotécnica, electrónica e informática
Computer engineering, Electrical engineering, Electronic engineering, Information engineering
topic Engenharia de computadores, Engenharia electrotécnica, electrónica e informática
Computer engineering, Electrical engineering, Electronic engineering, Information engineering
description Inductive Logic Programming (ILP) is a Machine Learning technique that has been quite successful in knowledge discovery for relational domains. ILP systems implemented in Prolog challenge the limits of Prolog systems due to heavy usage of resources such as database accesses and memory usage, and to very long execution times. The major reason to implement ILP systems in Prolog is that the inference mechanism implemented by the Prolog engine is fundamental to most ILP learning algorithms. ILP systems can therefore benefit from the extensive performance improvement work that has taken place for Prolog. On the other hand, ILP is a non-classical Prolog application because it uses large sets of ground facts and requires storing a large search tree. One major criticism of ILP systems is that they often have long running times. A technique that tries to tackle this problem is coverage caching [?]. Coverage caching stores previous results in order to avoid recomputation. Naturally, this technique uses the Prolog internal database to store results. The question is: does coverage caching successfully reduce the ILP systems running time? To obtain an answer to this question we evaluated the impact of the coverage caching technique using the April [?] ILP system with the YAP Prolog system. To understand the results obtained we profiled Aprils execution and present initial results. The contribution of this paper is twofold: to an ILP researcher it provides an evaluation of the coverage caching technique implemented in Prolog using well known datasets; to a Prolog implementation researcher it shows the need of efficient internal database indexing mechanisms.
publishDate 2003
dc.date.none.fl_str_mv 2003
2003-01-01T00:00:00Z
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url https://hdl.handle.net/10216/67438
dc.language.iso.fl_str_mv eng
language eng
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