A model framework-based domain-specific composable modeling method for combat system effectiveness simulation

Combat system effectiveness simulation (CoSES) plays an irreplaceable role in the effectiveness measurement of combat systems. According to decades of research and practice, composable modeling and multi-domain modeling are recognized as two major modeling requirements in CoSES. Current effectiveness simulation researches attempt to cope with the structural and behavioral complexity of CoSES based on a unified technological space, and they are limited to their existing modeling paradigms and fail to meet these two requirements. In this work, we propose a model framework-based domain-specific composable modeling method to solve this problem. This method builds a common model framework using application invariant knowledge for CoSES, and designs domain-specific modeling infrastructures for subdomains as corresponding extension points of the framework to support the modeling of application variant knowledge. Therefore, this method supports domain-specific modeling in multiple subdomains and the composition of subsystem models across different subdomains based on the model framework. The case study shows that this method raises the modeling abstraction level, supports generative modeling, and promotes model reuse and composability.     

Composing domain-specific physical models with general-purpose software modules in embedded control software

A considerable portion of software systems today are adopted in the embedded control domain. Embedded control software deals with controlling a physical system, and as such models of physical characteristics become part of the embedded control software. In current practices, usually general-purpose languages (GPL), such as C/C++ are used for embedded systems development. Although a GPL is suitable for expressing general-purpose computation, it falls short in expressing the models of physical characteristics as desired. This reduces not only the readability of the code but also hampers reuse due to the lack of dedicated abstractions and composition operators. Moreover, domain-specific static and dynamic checks may not be applied effectively. There exist domain-specific modeling languages (DSML) and tools to specify models of physical characteristics. Although they are commonly used for simulation and documentation of physical systems, they are often not used to implement embedded control software. This is due to the fact that these DSMLs are not suitable to express the general-purpose computation and they cannot be easily composed with other software modules that are implemented in GPL. This paper presents a novel approach to combine a DSML to model physical characteristics and a GPL to implement general-purpose computation. The composition filters model is used to compose models specified in the DSML with modules specified in the GPL at the abstraction level of both languages. As such, this approach combines the benefits of using a DSML to model physical characteristics with the freedom of a GPL to implement general-purpose computation. The approach is illustrated using two industrial case studies from the printing systems domain.     

Multi-perspective enterprise modeling: foundational concepts,prospects and future research challenges

The paper presents a method for multi- perspective enterprise modeling (MEMO) and a corresponding (meta-) modeling environment. An extensive analysis of requirements for enterprise modeling serves to motivate and assess the method. The method is based on an elaborate conception of multi-perspective enterprise models and on an extensible language architecture. The language architecture is comprised of a meta modeling language and an extensible set of integrated domain-specific modeling languages (DSML). The DSML are supplemented with process models and with guidelines for their reflective use. The corresponding modeling environment integrates editors for various DSML into multi-language model editors. It includes a meta model editor which enables the convenient use, development and extension of the set of supported DSML and supports the generation of respective graphical model editors. Thus, it also serves as a foundation for method engineering. MEMO covers both software engineering as well as social, managerial and economic aspects of the firm. The presentation of MEMO is supplemented with a comparative overview of other approaches to enterprise modeling. The paper concludes bys summarizing fundamental technical, epistemological and political challenges for enterprise modeling research and discusses potential paths for future research.     

Automating the construction of domain-specific modeling languages for object-oriented frameworks

The extension of frameworks with domain-specific modeling languages (DSML) has proved to be an effective way of improving the productivity in software product-line engineering. However, developing and evolving a DSML is typically a difficult and time-consuming task because it requires to develop and maintain a code generator, which transforms application models into framework-based code. In this paper, we propose a new approach for extending object-oriented frameworks that aims to alleviate this problem. The approach is based on developing an additional aspect-oriented layer that encodes a DSML for building framework-based applications, eliminating the need of implementing a code generator. We further show how a language workbench is capable of automating the construction of DSMLs using the proposed layer.     

Debugging measurement systems using a domain-specific modeling language

Capturing physical data in the context of measurement systems is a demanding process that often requires many repetitions with different settings. To assist in this activity, a domain-specific modeling language (DSML) called Sequencer has been developed to enable the improved definition of measurement procedures. With Sequencer, the level of abstraction has been raised and sophisticated changes in measurement procedures are now enabled. Although there are numerous DSMLs like Sequencer in the existing literature, there are some obstacles working against the more widespread adoption of DSMLs in practice. One challenge is the lack of supporting tools for DSMLs, which would improve the capabilities of end-users of such languages. For instance, support for debugging a model expressed in a DSML is often neglected. The lack of a debugger at the proper abstraction level limits the domain experts in discovering and locating bugs in a model. In this paper, Sequencer is presented together with debugging facilities (called Ladybird) that are integrated in a modeling environment. Ladybird supports different execution modes (e.g., steps, breakpoints, animations, variable views, and stack traces) that can be helpful during the debugging of a model. Ladybird's primary contribution is in showing the value of error detection in complicated industrial environments, such as data acquisition in automotive testing. The paper contributes to a discussion of the implementation details of DSML debugging facilities and how such a debugger can be reused to support domains other than the measurement context of Sequencer.     

A semi-formal description of migrating domain-specific models with evolving domains

One of the main advantages of defining a domain-specific modeling language (DSML) is the flexibility to adjust the language definition to changing requirements or in response to a deeper understanding of the domain. With the industrial applications of domain-specific modeling environments, models are valuable investments. If the modeling language evolves, these models must be seamlessly migrated to the evolved DSML. Although the changes stemming from the language evolution are not abrupt in nature, migrating existing models to a new language is still a challenging task. Our solution is the Model Change Language (MCL) tool set, which defines a DSML to describe the migration rules and then performs the model migration automatically. In this paper, we describe the precise semantics of MCL and its execution, along with the confluence of the migration.     

An object-oriented blackboard based approach for automated finite element modeling and analysis of multichip modules

This paper addresses the application of the blackboard system architecture and object-oriented data abstraction techniques to the domain of finite element modeling and analysis. Specifically, a hierarchical object-oriented database was used to represent the physical system at different levels of abstraction including the user-defined physical system level, a computer-generated, simplified physical model level, and the finite element model level. Object link relationships within a given abstraction level and across different abstraction levels resulted in seamless bidirectional information exchange. The blackboard system architecture employed provided a framework for distributed cooperative problem solving, for the application of simplifying domain-specific modeling assumptions, and for integrating the various software modules that are involved in the entire finite element modeling and analysis process. These methodologies were implemented in a prototype computational tool calledIMCMA theIntelligentMultichipModuleAnalyzer. An example illustrates howIMCMA automates finite element thermal analysis of small integrated circuit features in multichip modules through a two-step finite element submodeling process.     

Reusable abstractions for modeling languages

Model-driven engineering proposes the use of models to describe the relevant aspects of the system to be built and synthesize the final application from them. Models are normally described using Domain-Specific Modeling Languages (DSMLs), which provide primitives and constructs of the domain. Still, the increasing complexity of systems has raised the need for abstraction techniques able to produce simpler versions of the models while retaining some properties of interest. The problem is that developing such abstractions for each DSML from scratch is time and resource consuming.     

Formalizing the structural semantics of domain-specific modeling languages

Model-based approaches to system design are now widespread and successful. These approaches make extensive use of model structure to describe systems using domain-specific abstractions, to specify and implement model transformations, and to analyze structural properties of models. In spite of its general importance the structural semantics of modeling languages are not well-understood. In this paper we develop the formal foundations for the structural semantics of domain-specific modeling languages (DSML), including the mechanisms by which metamodels specify the structural semantics of DSMLs. Additionally, we show how our formalization can complement existing tools, and how it yields algorithms for the analysis of DSMLs and model transformations.

On the modeling and generation of service-oriented tool chains

Tool chains have grown from ad-hoc solutions to complex software systems, which often have a service-oriented architecture. With service-oriented tool integration, development tools are made available as services, which can be orchestrated to form tool chains. Due to the increasing sophistication and size of tool chains, there is a need for a systematic development approach for service-oriented tool chains. We propose a domain-specific modeling language (DSML) that allows us to describe the tool chain on an appropriate level of abstraction. We present how this language supports three activities when developing service-oriented tool chains: communication, design and realization. A generative approach supports the realization of the tool chain using the service component architecture. We present experiences from an industrial case study, which applies the DSML to support the creation of a service-oriented tool chain. We evaluate the approach both qualitatively and quantitatively by comparing it with a traditional development approach.     

Synthesizing interpreted domain-specific models to manage smart microgrids

The increase in prominence of model-driven software development (MDSD) has placed emphasis on the use of domain-specific modeling languages (DSMLs) during the development process. DSMLs allow for domain concepts to be conceptualized and represented at a high level of abstraction. Currently, most DSML models are converted into high-level languages (HLLs) through a series of model-to-model and/or model-to-text transformations before they are executed. An alternative approach for model execution is the interpretation of models directly without converting them into an HLL. These models are created using interpreted DSMLs (i-DSMLs) and realized using a semantic-rich execution engine or domain-specific virtual machine (DSVM).In this article we present an approach for model synthesis, the first stage of model interpretation, that separates the domain-specific knowledge (DSK) from the model of execution (MoE). Previous work on model synthesis tightly couples the DSK and MoE reducing the ability for implementations of the DSVM to be easily reused in other domains. To illustrate how our approach to model synthesis works for i-DSMLs, we have created MGridML, an i-DSML for energy management in smart microgrids, and an MGridVM prototype, the DSVM for MGridML. We evaluated our approach by performing experiments on the model synthesis aspect of MGridVM and comparing the results to a DSVM from the user-centric communication domain.     

DSML4CP: A Domain-specific Modeling Language for Concurrent Programming

Nowadays, concurrent programs are an inevitable part of many software applications. They can increase the computation performance of the applications by parallelizing their computations. One of the approaches to realize the concurrency is using multi thread programming. However, these systems are structurally complex considering the control of the parallelism (such as thread synchronization and resource control) and also considering the interaction between their components. So, the design of these systems can be difficult and their implementation can be error-prone especially when the addressed system is big and complex. On the other hand, a Domain-specific Modeling Language (DSML) is one of the Model Driven Development (MDD) approaches which tackles this problem. Since DSMLs provide a higher abstraction level, they can lead to reduce the complexities of the concurrent programs. With increasing the abstraction level and generating the artifacts automatically, the performance of developing the software (both in design and implementation phases) is increased, and the efficiency is raised by reducing the probability of occurring errors. Thus, in this paper, a DSML is proposed for concurrent programs, called DSML4CP, to work in a higher level of abstraction than code level. To this end, the concepts of concurrent programs and their relationships are presented in a metamodel. The proposed metamodel provides a context for defining abstract syntax, and concrete syntax of the DSML4CP. This new language is supported by a graphical modeling tool which can visualize different instance models for domain problems. In order to clarify the expressions of the language; the static semantic controls are realized in the form of constraints. Finally, the architectural code generation is fulfilled via model transformation rules using the templates of the concurrent programs. To increase level of the DSML׳s leverage and to demonstrate the general support of concurrent programming by the DSML, the transformation mechanism of the tool supports two well-known and highly used programming languages for code generation; Java and C#. The performed experiments on two case studies indicate a high performance for proposed language. In this regard, the results show automatic generation of 79% of the final code and 86% of the functions/modules on average.     

Data-driven modeling and simulation framework for material handling systems in coal mines

In coal mining industry, discrete-event simulation has been widely used to support decisions in material handling system (MHS) to achieve premiums on revenues. However, the conventional simulation modeling approach requires extensive expertise of simulation during the modeling phase and lacks flexibility when the MHS structure changes. In this paper, a data-driven modeling and simulation framework is developed for MHS of coal mines to automatically generate a discrete-event simulation model based on current MHS structural and operational data. To this end, a formal information model based on Unified Modeling Language (UML) is first developed to provide MHS structural information for simulation model generation, production information for simulation execution, and output requirement information for defining simulation outputs. Then, Petri net-based model generation procedures are designed and used to automatically generate a simulation model in Arena® based on the simulation inputs conforming to the constructed information model. The proposed framework is demonstrated for one of the largest open-pit coal mines in the USA, and it has been demonstrated that the framework can be used to effectively generate the simulation models that precisely represent MHS of coal mines, and then be used to support various decisions in coal mining such as equipment scheduling.     

User-oriented problem abstractions in scheduling

In this paper we describe a modeling framework aimed at facilitating the customization and deployment of artificial intelligence (AI) scheduling technology in real-world contexts. Specifically, we describe an architecture aimed at facilitating software product line development in the context of scheduling systems. The framework is based on two layers of abstraction: a first layer providing an interface with the scheduling technology, on top of which we define a formalism to abstract domain-specific concepts. We show how this two-layer modeling framework provides a versatile formalism for defining user-oriented problem abstractions, which is pivotal for facilitating interaction between domain experts and technologists. Moreover, we describe a graphical user interface (GUI)-enhanced tool which allows the domain expert to interact with the underlying core scheduling technology in domain-specific terms. This is achieved by automatically instantiating an abstract GUI template on top of the second modeling layer.     

A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems

Multi-agent systems (MASs) include multiple interacting agents within an environment to provide a solution for complex systems that cannot be easily solved with individual agents or monolithic systems. However, the development of MASs is not trivial due to the various agent properties such as autonomy, responsiveness, and proactiveness, and the need for realization of the many different agent interactions. To support the development of MASs various domain-specific modeling languages (DSMLs) have been introduced that provide a declarative approach for modeling and supporting the generation of agent-based systems. To be effective, the proposed DSMLs need to meet the various stakeholder concerns and the related quality criteria for the corresponding MASs. Unfortunately, very often the evaluation of the DSML is completely missing or has been carried out in idiosyncratic approach. If the DSMLs are not well defined, then implicitly this will have an impact on the quality of the MASs. In this paper, we present an evaluation framework and systematic approach for assessing existing or newly defined DSMLs for MASs. The evaluation is specific for MAS DSMLs and targets both the language and the corresponding tools. To illustrate the evaluation approach, we first present SEA_ML, which is a model-driven MAS DSML for supporting the modeling and generation of agent-based systems. The evaluation of SEA_ML is based on a multi-case study research approach and provides both qualitative evaluation and quantitative analysis. We report on the lessons learned considering the adoption of the evaluation approach as well as the SEA_ML for supporting the generation of agent-based systems.     

Task structures as a basis for modeling knowledge-based systems

Recently, there has been an increasing interest in improving the reliability and quality of AI systems. As a result, a number of approaches to knowledge-based systems modeling have been proposed. However, these approaches are limited in formally verifying the intended functionality and behavior of a knowledge-based system. In this article, we proposed a formal treatment to task structures to formally specify and verify knowledge-based systems modeled using these structures. The specification of a knowledge-based system modeled using task structures has two components: a model specification that describes static properties of the system, and a process specification that characterizes dynamic properties of the system. The static properties of a system are described by two models: a model about domain objects (domain model), and a model about the problem-solving states (state model). The dynamic properties of the system are characterized by (1) using the notion of state transition to explicitly describe what the functionality of a task is, and (2) specifying the sequence of tasks and interactions between tasks (i.e., behavior of a system) using task state expressions (TSE). The task structure extended with the proposed formalism not only provides a basis for detailed functional decomposition with procedure abstraction embedded in, but also facilitates the verification of the intended functionality and behavior of a knowledge-based system. © 1997 John Wiley & Sons, Inc.     

Proactive modeling: a new model intelligence technique

This article discusses a model intelligence technique called proactive modeling. The goal of proactive modeling is to reduce the amount of manual modeling required when using a graphical DSML and to assist in step-by-step creation of a model. Proactive modeling accomplishes this goal by examining the metamodels syntax and constraints, automatically executing model modifications, and prompting the modeler for assistance when more than one valid model modification exists, but none are necessary. We have integrated proactive modeling into the generic modeling environment (GME) as a generic add-on that can operate on any domain-specific modeling language implemented in GME. Lastly, results from applying proactive modeling to several DSMLs in GME show that it can reduce modeling effort.     

面向业务问题求解的知识资源特定领域建模方法

针对企业中解决业务问题时知识资源利用不合理的问题,提出一种面向业务问题求解的知识资源特定领域模型。首先从领域纲要出发,将业务问题求解领域的基本元素描述出来,并详细分析其中对象之间的关系;然后通过在领域模板中建立的问题-知识事件驱动过程链(PK-EPC)模型对问题求解的流程进行格式化,并匹配到相应的知识单元;其次是通过对应的应用模型对业务问题进行分解,并将前面的知识单元与知识载体匹配。根据前面的模型可得出可供企业选择的集成业务活动、知识单元和知识载体的多层次求解方案模型,为企业业务问题求解提供了快捷准确的方法。最后基于Java设计了面向业务问题求解的企业求解方案建模系统,通过实例验证了该模型的可行性。