Worlds of Events Deduction with Partial Knowledge about Causality

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Haeri, SH (Author) Other; Van Roy, P (Author) Other; Baquero, C (Author) Other; Meiklejohn, C (Author) Other

Journal

Title: Electronic Proceedings in Theoretical Computer Science, EPTCS

Vol. abs/1608.03326

Pages: 113-127

ISSN: 2075-2180

Publisher: Open Publishing Association

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00M-BDG

DOI: 10.4204/eptcs.223.8

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15