SimQRi - A Query-oriented Tool for the Efficient Simulation and Analysis of Process Models (Tool Demonstration) Christophe Ponsard * , Quentin Boucher * , Gustavo Ospina * * CETIC Research Centre, Belgium Email: {christophe.ponsard, quentin.boucher, gustavo.ospina}@cetic.be Abstract—Process models are an abstraction used in several domains such as manufacturing (transformation chains), logis- tics (procurement and distribution networks), architecture of electronics systems (network of data/computation nodes). Such systems are often subject to requirements related to the proces- sing delay, throughput, overall reliability, or quality attributes of specific outputs. Those characteristics are highly dynamic. Assessing them at design time requires some kind of execution of the model, typically using simulation. As the system is often non- deterministic, several simulations need to be run and combined in order to draw relevant conclusions. In this paper, we describe a tool, called SimQRi, that we developed to efficiently run a large number of simulations over process models, using Discrete Event Simulation combined with Monte-Carlo techniques. Their key point is that the properties to be assessed are formulated as queries over the model with a trace semantics. Queries are evaluated and aggregated through simulations, so there is no need to store traces and perform post-processing on them. Several operators are available on different process-related components (storage content, process activity, number of processes items, etc). In our demo we will demonstrate how the tool can be used 1) to assess several risks on supply chains and 2) to design a green Cloud to cope with response times with optimal energy usage. Index Terms—Process Models; Risk Assessment; Discrete- Event Simulation; Oscar.cbls; I. I NTRODUCTION Process models are very common abstractions in many application domains both in the physical world (logistics, supply chain domains), in computer world (Cloud architecture, signal processing, etc), or even in hybrid domains such as smart manufacturing heavily mixing physical processes with IT data collection and analysis processes based on the Internet of Things and Big Data. Reasoning on such processes is not always easy because of the dynamic nature of the requirements to enforce. It can require some form of prototyping already beyond the design phase. At design time, an option is to use model- checking, e.g. using the Communicating Sequential Process (CSP) abstraction and tools like FDR [1], [2]. However, such approaches have some limitations in expressiveness and size of manageable models. We consider a more practical approach based on model simulation. Our primary motivation is to help small and medium enterprises (SMEs) in improving their maturity level to master the processes present in their domain, focusing on supply chains as primary domain, as confirmed by a survey we conducted [3]. Based on this, we developed a framework and its supporting tooling composed of: • a modelling language to represent process models using quite abstract building bricks (processes, flows, storages) so it can be used in different application domains. • an expressive query language to capture a large variety of quantifiable properties. • an editor tool supporting graphical modelling and tabular capture of queries, including the specification of stochas- tic parameters for most of the parameters and model validation, as well as feedback at the user interface level. • a simulation engine using Discrete Event Simulation (DES) and Monte Carlo Simulation (MCS) in order to cope with the non-determinism present inside the model. • a reporting tool to analyse the simulation results. The key point of such an approach is to reach a high level of efficiency. In order to reach that goal, the tool is designed to compute all the queries during the simulation without the need to store any trace and post-process on them. The ultimate goal of our research project is to help companies to fulfil strategic goals, minimize financial, reputational and productivity losses and improve their overall productivity and reliability. Our tool is available at https://simqri.cetic.be both as an online web-based application (requiring no installation) and as an Eclipse plugin. This paper is structured as follows. Section 2 gives a summary of the framework in terms of meta-model, query language and architecture. Section 3 details a use case related to supply chain risk management while Section 4 details another case related to green cloud design. Finally Section 5 draws some conclusions and our development roadmap. II. FRAMEWORK DESCRIPTION Our framework is composed of the following elements: • a Domain Specific Language (DSL) based on a meta- model able to capture all the main elements such as suppliers, storages, processes, and flows. • an expressive Query Language able the capture a rich set of properties. Those properties can be efficiently mea- sured and statistically processed during the simulation. • an architecture based on an Open Source simulation engine and both a web-based and desktop-based user interface. 38