Worlds of Events Deduction with Partial Knowledge about Causality

Detalhes bibliográficos
Autor(a) principal: Haeri,SH
Data de Publicação: 2016
Outros Autores: Van Roy,P, Carlos Baquero, Meiklejohn,C
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/7485
http://dx.doi.org/10.4204/eptcs.223.8
Resumo: Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/ removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.
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spelling Worlds of Events Deduction with Partial Knowledge about CausalityInteractions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/ removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.2018-02-14T15:47:17Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/7485http://dx.doi.org/10.4204/eptcs.223.8engHaeri,SHVan Roy,PCarlos BaqueroMeiklejohn,Cinfo: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:RCAAP2023-05-15T10:20:19Zoai:repositorio.inesctec.pt:123456789/7485Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:56.813186Repositório 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 Worlds of Events Deduction with Partial Knowledge about Causality
title Worlds of Events Deduction with Partial Knowledge about Causality
spellingShingle Worlds of Events Deduction with Partial Knowledge about Causality
Haeri,SH
title_short Worlds of Events Deduction with Partial Knowledge about Causality
title_full Worlds of Events Deduction with Partial Knowledge about Causality
title_fullStr Worlds of Events Deduction with Partial Knowledge about Causality
title_full_unstemmed Worlds of Events Deduction with Partial Knowledge about Causality
title_sort Worlds of Events Deduction with Partial Knowledge about Causality
author Haeri,SH
author_facet Haeri,SH
Van Roy,P
Carlos Baquero
Meiklejohn,C
author_role author
author2 Van Roy,P
Carlos Baquero
Meiklejohn,C
author2_role author
author
author
dc.contributor.author.fl_str_mv Haeri,SH
Van Roy,P
Carlos Baquero
Meiklejohn,C
description Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/ removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2018-02-14T15:47:17Z
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http://dx.doi.org/10.4204/eptcs.223.8
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