Changes of professional environment, caused by introduction of new technologies and techniques, create a necessity in continuous education and development of professional competences. In these conditions, managers and other company employees face the choice of methods and tools of personnel training. A business game is one of the most productive tools of business-education This paper elaborates the ideas embodied previously, which considered the conceptual approach to a toolkit creation for active training techniques in a form of competence-based business game studio. Competence-based business game studio is an ergatic system for development of professional competences, which are required to ensure organization’s business processes. Business game control is performed with the help of automate module, which executes the interpretation of expressions in algorithm logical scheme language. Unified business process is the input data for automate model construction. Unified business process is acquired from the information about company’s real business processes, represented in poorly formalized ways. Unified business process transforms into unified training business process, which includes possible business game trainee actions and information about input, output, administrative data and business process operation execution mechanism. The next step to automate model is the construction of scenario graph, which represents a more formalized description of unified training business process. Next, the administrating algorithm of the business game in algorithm logical scheme language is built. The paper considers the algorithms of transition between models that allow to automate the process of acquiring algorithm logical scheme in order to implement the business games scenario.
Abstract: An approach to reengineering business processes through the integration of the domain specific modeling platform and Process Mining tools is described. An analysis of the existing approaches to business processes improvement is presented and restrictions are shown. The Process Mining methods are related to business process reengineering stages and tasks. Comparative analysis of Process Mining tools is executed. The advantages of the using of domain specific modeling tools (language workbenches, DSM platforms) are substantiated. Brief comparison of various visual languages notations and model transformation examples are described. The DSM platform ensures mutual understanding between specialists. The MetaLanguage DSM platform is the basis of integration tools. Some DSL (metamodels) are described and transformations are illustrated. The implementation of integrated tools reduces the complexity of analyst’s work. Keywords: business processes reengineering, domain specific modeling, DSM, modeling languages, DSL, language toolkits, DSM platform, model transformations, business process analysis, Process Mining. ACM Classification Keywords: H.4 INFORMATION SYSTEMS APPLICATIONS: H.4.1 Office Automation –Workflow management; H.4.2 Types of Systems – Decision support (e.g., MIS). I.6 SIMULATION AND MODELING: I.6.2 Simulation Languages; I.6.4 Model Validation and Analysis; I.6.5 Model Development Modeling methodologies.
Abstract: The approach to models generation automation and implementation of multifaceted business process modeling on the basis of graphical model transformation is described. To create graphical models of diverse notations (diagrams in notations of visual modeling languages) one can exploit visual modeling software tools and language workbenches, DSM platforms. Domain specific modeling tools allow simplifying model design process, to involve domain experts (they are not masters of information technologies and have not programming skills) to formal model development. Newly-created models can be converted into simulation models or specific analytical models with the model transformation tools. Therefore, at new task solving process with modeling tools modelers have not to duplicate model development with new tools in new language notation. Model designers can use most suitable tools and most expressive languages for models development in their domain to solve their tasks. Obtained models after transformation can be examined with means of specific simulation modeling systems including, for instance, AnyLogic, or with mathematical software packages such as Mathcad, Maple or Mathematica. The visual business process modeling notation choice is substantiated. Mathematical model named DFD-graph is used as mathematical basis of model generation tools. The normalization rules form the backbone to the DFD business process model normalization. This algorithm is the basis of automating model generation software implementation. Keywords: business process modeling, visual modeling languages, business process analysis, mathematical modeling, model development automation, model transformations, model reusing. ACM Classification Keywords: D.2 SOFTWARE ENGINEERING: D.2.2 Design Tools and Techniques – Computer-aided software engineering (CASE), Programmer workbench; D.2.13 Reusable Software – Domain engineering, Reuse models. I.6 SIMULATION AND MODELING: I.6.2 Simulation Languages; I.6.3 Applications; I.6.4 Model Validation and Analysis; I.6.5 Model Development. G.4 MATHEMATICAL SOFTWARE: Algorithm design and analysis, User interfaces.
This paper focuses on the problem of validation and verification of computer network simulation models. Authors propose to use special linguistic and program tools of CAD system TriadNS in this case. First of all it should be noted that TRiadNS is a computer system which was developed for computer network design. Simulation is the main method for investigation of designed computer networks. But it is very important to have a credible simulation result. It is necessary for target users to have sufficient confidence that results generated by a simulation run reflect real world operation to a large degree. Authors observe the specifications of the simulation model in TriadNS.Net, consider the program tools for simulation model analysis (information procedures and conditions of simulation) and propose to use them for simulation model validation and verification, debugging and testing. Besides, the authors suggest program tools including the intellectual agents and ontology for localization of mistakes determined during verification and validation processes. Moreover the authors show how the specific features of hierarchical simulation models in TRIADNS make the process of testing and debugging of simulation models flexible.
This paper describes the problem of automated pollen grains image recognition using images from microscope. This problem is relevant because it allows to automate a complex process of pollen grains classification and to determine the beginning of pollen dispersion which cause an the allergic responses. The main recognition methods are Hamming network [Korotkiy, 1992] and structural approach [Fu, 1977]. The paper includes Hamming network advantages over Hopfield network [Ossowski, 2000]. The steps of preprocessing (noise filtering, image binarization, segmentation) use OpenCV [Bradsky et al, 2008] functions and the feature point method [Bay et al, 2008]. The paper describes both preprocessing algorithms and main recognition methods. The experiments results showed a relative efficiency of these methods. The conclusions about methods productivity based on errors of type I and II. The paper includes alternative recognition methods which are planning to use in the follow up research.
We study the problem of testing composite hypotheses versus composite alternatives when there is a slight deviation between the model and the real distribution. The used approach, which we called sub-optimal testing, implies an extension of the initial model and a modification of a sequential statistically significant test for the new model. The sub-optimal test is proposed and a non-asymptotic border for the loss function is obtained. Also we investigate correlation between the sub-optimal test and the sequential probability ratio test for the initial model.
Abstract: This paper discusses the problems of the agent-based simulation system design. It is well known that agent models extend the capabilities of simulation for solving some problems that can’t be solved by the methods of system dynamics and discrete event simulation. Particular attention in the design and the implementation of agent-based simulation authors pay to the problems of distributed simulation and the problems of intelligent agent’s implementation and the use of ontologies at all stages of the simulation experiments. Keywords: simulation, agent-based model, distributed simulation, intelligent agent.
The problems of investigations of routing algorithms and data transfer algorithms in mobile self-controlled networks by simulation methods are considered. This class of networks has specific properties: dynamically moving nodes, "limited distance" between nodes, and the absence of a centralized node. The mathematical model of such a network is a dynamic graph. It is important to predict the conditions when the connection failure of nodes occurs during the execution of various routing algorithms. Simulation experiments are performed in the AnyLogic environment.