2009
DOI: 10.1093/comjnl/bxp052
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Events, Causality and Symmetry

Abstract: The article discusses causal models, such as Petri nets and event structures, how they have been rediscovered in a wide variety of recent applications, and why they are fundamental to computer science. A discussion of their present limitations leads to their extension with symmetry. The consequences, actual and potential, are discussed.

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Cited by 24 publications
(9 citation statements)
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References 89 publications
(127 reference statements)
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“…General approaches to the denotational or operational semantics of effects [36,34] provide an intriguing starting point for the study of provenance in the presence of effects. Ideas from concurrency theory, particularly Winskel's event structures [58], may be a good place to start in understanding the meaning of provenance in concurrent or distributed settings.…”
Section: Related Workmentioning
confidence: 99%
“…General approaches to the denotational or operational semantics of effects [36,34] provide an intriguing starting point for the study of provenance in the presence of effects. Ideas from concurrency theory, particularly Winskel's event structures [58], may be a good place to start in understanding the meaning of provenance in concurrent or distributed settings.…”
Section: Related Workmentioning
confidence: 99%
“…The (in-)dependency relations are inspired by Mazurkiewicz trace equivalences [4], which capture both aspects. The conflict relation is inspired by the theory of event structures [11]. The enable relation is inspired by dynamic partial order reduction [7].…”
Section: Related Workmentioning
confidence: 99%
“…Structures with a contact or a proximity relation are studied in topology, in region based theory of space and, possibly with different terminology, in algebraic logic and computer science [1,2,3,4,5,6,7,8,10,11,12,13,14,15,16]. As a basic example, if X is a topological space with closure K, the binary relation δ defined by x δ y if Kx ∩ Ky = ∅, for x, y ⊆ X is a proximity, called the standard proximity [3,Example 2.1.3].…”
Section: Introductionmentioning
confidence: 99%