Stochastic Graph Transformation Systems

Heckel, Reiko; Lajios, Georgios; Menge, Sebastian

doi:10.1007/978-3-540-30203-2_16

Cited by 38 publications

(47 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heckel et al propose in [17] the modeling and analysis of stochastic graphs transformation systems by defining Continuous Time Markov chains from GTSs with transition matrices representing the probabilities of the application of each rule. They provide some support using GROOVE [28] and PRISM.…”

Section: Related Workmentioning

confidence: 99%

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Durán

Moreno-Delgado

Álvarez-Palomo

2016

Fundamental Approaches to Software Engineering

View full text Add to dashboard Cite

Abstract. Domain experts may use novel tools that allow them to design and model their systems in a notation very close to the domain problem. However, the use of tools for the statistical analysis of stochastic systems requires software engineers to carefully specify such systems in low level and specific languages. In this work we line up both scenarios, specific domain modeling and statistical analysis. Specifically, we have extended the e-Motions system, a framework to develop real-time domain-specific languages where the behavior is specified in a natural way by in-place transformation rules, to support the statistical analysis of systems defined using it. We discuss how restricted e-Motions systems are used to produce Maude corresponding specifications, using a model transformation from e-Motions to Maude, which comply with the restrictions of the VeStA tool, and which can therefore be used to perform statistical analysis on the stochastic systems thus generated. We illustrate our approach with a very simple messaging distributed system.

show abstract

Section: Related Workmentioning

confidence: 99%

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Durán

Moreno-Delgado

Álvarez-Palomo

2016

Fundamental Approaches to Software Engineering

View full text Add to dashboard Cite

show abstract

“…Regarding point 1, we propose a notion of stochastic marked graph rewriting which follows the general guidelines of the theory of graph transformation systems (GTS) [16,7,15,29,6,25]. Stochastic processes are modelled as rewrite rules over directed multigraphs with marks allowing for pre-and post-conditions on node degrees.…”

Section: Our Goalmentioning

confidence: 99%

“…We start with preliminaries on directed multigraphs, followed by a brief summary of the DPO approach and its stochastic semantics [25]. Next, we introduce marked graphs as a means to add simple application conditions to graph rewrite rules.…”

Section: Reversible Stochastic Graph Rewritingmentioning

confidence: 99%

Moment Semantics for Reversible Rule-Based Systems

Danos

Heindel

Honorato-Zimmer

et al. 2015

Reversible Computation

View full text Add to dashboard Cite

Abstract. We develop a notion of stochastic rewriting over marked graphs -i.e. directed multigraphs with degree constraints. The approach is based on double-pushout (DPO) graph rewriting. Marked graphs are expressive enough to internalize the 'no-dangling-edge' condition inherent in DPO rewriting. Our main result is that the linear span of marked graph occurrence-counting functions -or motif functions -form an algebra which is closed under the infinitesimal generator of (the Markov chain associated with) any such rewriting system. This gives a general procedure to derive the moment semantics of any such rewriting system, as a countable (and recursively enumerable) system of differential equations indexed by motif functions. The differential system describes the time evolution of moments (of any order) of these motif functions under the rewriting system. We illustrate the semantics using the example of preferential attachment networks; a well-studied complex system, which meshes well with our notion of marked graph rewriting. We show how in this case our procedure obtains a finite description of all moments of degree counts for a fixed degree.

show abstract

“…In [9], GT rules are extended with stochastic delays given by a negative exponential distribution. A rule with stochastic delay p = L τ → R has similar semantics to a delayed rule, but the difference concerns the memory policy when it is executed.…”

Section: Fig 8 Activities (Left) Rules With Delays (Center) Stochmentioning

confidence: 99%

“…The work of [9] takes concepts from stochastic Petri nets, so that rules are assigned a delay given by a negative exponential distribution. An important difference is that, while time is assigned to rules in [4,9,17], we assign it to schedulings. Hence, while they interpret rules as activities with unobservable initiation, we interpret rules as events, making our approach able to model all of them in a unified way.…”

Section: Related Workmentioning

confidence: 99%

Graph Transformation for Domain-Specific Discrete Event Time Simulation

Lara

Guerra

Boronat

et al. 2010

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Graph transformation is being increasingly used to express the semantics of domain specific visual languages since its graphical nature makes rules intuitive. However, many application domains require an explicit handling of time in order to represent accurately the behaviour of the real system and to obtain useful simulation metrics.Inspired by the vast knowledge and experience accumulated by the discrete event simulation community, we propose a novel way of adding explicit time to graph transformation rules. In particular, we take the event scheduling discrete simulation world view and incorporate to the rules the ability of scheduling the occurrence of other rules in the future. Hence, our work combines standard, efficient techniques for discrete event simulation (based on the handling of a future event set) and the intu-itive, visual nature of graph transformation. Moreover, we show how our formalism can be used to give semantics to other timed approaches.

show abstract

Stochastic Graph Transformation Systems

Cited by 38 publications

References 18 publications

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Statistical Model Checking of e-Motions Domain-Specific Modeling Languages

Moment Semantics for Reversible Rule-Based Systems

Graph Transformation for Domain-Specific Discrete Event Time Simulation

Contact Info

Product

Resources

About