SimSG: Rule-based Simulation using Stochastic Graph Transformation

By: Sebastian Ehmes, Lars Fritsche, Andy Schürr

Abstract

Stochastic models can be found in various domains. For example, biochemical processes such as molecular interactions or the dynamics of wireless network topologies, where changes occur with certain probabil- ities. Having the ability to simulate scenarios in these domains can be crucial when real-life observations of certain processes are infeasible, e.g., protein-protein interactions in biochemistry, or expensive, e.g., building large wireless networks for research purposes. Stochastic graph transfor- mation systems provide the means to describe the structure and simulate the behavior of such probability-driven environments in an adequate way, by modelling the state transitions using graph transformation rules, whose application depends on the current state and their application probabili- ties. To the best of our knowledge, there is currently no general-purpose simulation tool available anymore that performs rule-based simulations using stochastic graph transformation. Therefore, we developed SimSG a modular stochastic simulation tool that addresses the needs of a wide range of application domains - in contrast to most specialized simulation tools that are limited to one domain only. To facilitate the versatility of the tool, SimSG can be configured to employ different general-purpose tools for incremental graph pattern matching (currently, Democles and Viatra). We evaluate SimSG based on two use cases: First, using an example of the biochemistry domain, we conduct a comparative evaluation against the domain-specific tool KaSim. Second, we underpin the general-purpose applicability of SimSG by analyzing the simulation of a wireless sensor network scenario.

Keywords

Stochastic Graph Transformation, Model-driven Development, Rule-based Simulation

Cite as:

Sebastian Ehmes, Lars Fritsche, Andy Schürr, “SimSG: Rule-based Simulation using Stochastic Graph Transformation”, Journal of Object Technology, Volume 18, no. 3 (July 2019), pp. 1:1-17, doi:10.5381/jot.2019.18.3.a1.