A case study on the use of a conceptual modeling framework for construction simulation

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.

Conceptual model is considered to be essential in simulation development activities. However, there are only a few research studies on how to develop conceptual models, the problems of model's low reusability and lacking management are presented in the conceptual model's development of simulation system. To solve the above problems, the concept of conceptual model (MCM) is presented for realizing the conceptual model's abstract in the higher arrangement. The ontology conception is introduced to design the MCM, and the MCM's architecture and modeling process are given too. By mapping the MCM to conceptual model (CM), the conceptual model's modeling process is realized. Finally, the method is applied in the conceptual model's modeling of equipment support simulation system, and the better validation is obtained. Conceptual Model. Meta means overtaking or basic, which often be used with other nouns such as language, model and data. The concepts of language, model and data are formed. When developing the conceptual models of some specific domains, the meta may be used before CM, and the concept of conceptual model (MCM) is presented. In brief, MCM is the model of CM and has the higher abstract degree. By using the MOF (Meta Object Facility) for reference (3), the four levels MCM architecture is presented, which is shown in Table 1. Table 1 MCM's Architecture

Writing research can be challenging--especially writing the conceptual model. Deciding how much to include of the conceptual model and where to place it is difficult. This experienced research author presents four steps to guide the nurse author in writing the conceptual model for research reports.

Engaging stakeholders in policy making and supporting policy development with advanced information and communication technologies including policy simulation is currently high on the agenda of research. In order to involve stakeholders in providing their input to policy modeling via online means, simple techniques need to be employed such as scenario technique. Scenarios enable stakeholders to express their views in narrative text. At the other end of policy development, a frequently used approach to policy modeling is agent-based simulation. So far, effective support to transform narrative text input to formal simulation statements is not widely available. In this paper, we present a novel approach to support the transformation of narrative texts via conceptual modeling into formal simulation models. The approach also stores provenance information which is conveyed via annotations of texts to the conceptual model and further on to the simulation model. This way, traceability of information is provided, which contributes to better understanding and transparency, and therewith enables stakeholders and policy modelers to return to the sources that informed the conceptual and simulation model. In this paper, we present the consistent conceptual description (CCD) as conceptual modeling approach to bridge the gap between narrative texts and formal policy models. The CCD meta-model with the underlying vocabulary for describing policy contexts is detailed. A case study introduces the application of the approach in the Open Collaboration for Policy Modeling project.

Conceptual modeling is widely recognized to be the necessary foundation for building a database that is well-documented and fully satisfies the user requirements. In particular, from the designer point of view the availability of a conceptual model provides a higher level of abstraction in describing the warehousing process and its architecture in all its aspects. Typically conceptual models rely on a graphical notation that facilitates writing, understanding, and managing conceptual schemata by both designers and users. The Entity/Relationship (E/R) model (Chen, 1976) is widespread in the enterprises as a conceptual formalism to provide standard documentation for relational information systems; nevertheless, as E/R is oriented to support queries that navigate associations between data rather than synthesize them, it is not well-suited for data warehousing (Kimball, 1998). Actually, the E/R model has enough expressivity to represent most concepts necessary for modeling a Data Warehouse (DW); on the other hand, in its basic form, it is not able to properly emphasize the key aspects of the multidimensional model, so that its usage for DWs is expensive from the point of view of the graphical notation and not intuitive (Rizzi, 2006). Some designers claim that star schemata are expressive enough for conceptual modeling. Actually, a star schema is just a (denormalized) relational schema, so it merely defines a set of relations and integrity constraints. Using star schema for conceptual modeling is like starting to build a complex software by writing the code, without the support of any static, functional, or dynamic model, which typically leads to very poor results from the points of view of adherence to user requirements, maintenance, and reuse. For all these reasons, in the last few years the research literature has proposed several original approaches for modeling a DW, some based on extensions of known conceptual formalisms (e.g. E/R, Unified Modeling Language (UML)), some based on ad hoc ones. Remarkably, a comparison of the different models made by Abello (2006) pointed out that, abstracting from their graphical form, the core expressivity is similar, thus proving that the academic community reached an informal agreement on the required expressivity. This paper discusses the expressivity of an ad hoc conceptual model, the Dimensional Fact Model (DFM), in order to let the user verify the usefulness of a conceptual modeling step in DW design. After a brief listing of the main conceptual model proposals, the basic and advanced features in DW conceptual modeling are introduced and described by examples. Finally, the current trends in DW conceptual modeling are reported and the conclusions are drawn.

Bringing together an international group of researchers involved in military, business, and health modeling and simulation, Conceptual Modeling for Discrete-Event Simulation presents a comprehensive view of the current state of the art in the field. The book addresses a host of issues, including: What is a conceptual model? How is conceptual modeling performed in general and in specific modeling domains? What is the role of established approaches in conceptual modeling? Each of the books six parts focuses on a different aspect of conceptual modeling for simulation. The first section discusses the purpose and requirements of a conceptual model. The next set of chapters provides frameworks and tools for conceptual modeling. The book then describes the use of soft systems methodology for model structuring as well as the application of software engineering methods and tools for model specification. After illustrating how conceptual modeling is adopted in the military and semiconductor manufacturing, the book concludes with a discussion on future research directions. This volume offers a broad, multifaceted account of the field by presenting diverse perspectives on what conceptual modeling entails. It also provides a basis upon which these perspectives can be compared.

It is laborious and time consuming to build domain ontology, which models a specific domain and specifies the concepts in a particular subject. In this paper, a 5W1H-based conceptual modeling framework for domain ontology is proposed, which is used to analysis domain concepts and relations from six aspects including Who, When, Where, What, Why and How. The definition of domain ontology, domain concept and domain relation are also presented. According to the framework, the conceptual model of Science & Technology Project Ontology (STPO) in science and technology domain is designed, in which main concepts and relations within the STPO are clearly described. From the analysis, the 5W1H conceptual modeling framework can be mapped to the class model in Object-Oriented method, which is used to model things in real world. Application shows that the framework is effective to model domain knowledge and scalable to business changes and user requirements.

The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

Conceptual modeling is the abstraction of a simulation model from the part of the real world it is representing; in other words, choosing what to model, and what not to model. This is generally agreed to be the most difficult, least understood and most important task to be carried out in a simulation study. In this tutorial the problem of conceptual modeling is first illustrated through an example of modeling a hospital clinic. We define the term ‘conceptual model’ and go on to identify the artefacts of conceptual modeling and hence the role of conceptual modeling in the simulation project life-cycle. The discussion then focuses on the requirements of a conceptual model, the benefits and approaches for documenting a conceptual model, and frameworks for guiding the conceptual modeling activity. One specific framework is described and illustrated in more detail. The tutorial concludes with a discussion on the level of abstraction.

Conceptual modeling is an important step for successful system development. It helps system designers and business practitioners share the same view on domain knowledge. If the work is successful, a result of conceptual modeling can be beneficial in increasing productivity and reducing failures. However, the value of conceptual modeling is unlikely to be evaluated uniformly because we are lack of agreement on how to elicit concepts and how to represent those with conceptual modeling constructs. Especially, designing relationships between components, also known as part-whole relationships, have been regarded as complicated work. The recent study, Representing Part-Whole Relations in Conceptual Modeling : An Empirical Evaluation (Shanks et al., 2008), published in MIS Quarterly, can be regarded as one of positive efforts. Not only the study is one of few attempts of trying to clarify how to select modeling alternatives in part-whole design, but also it shows results based on an empirical experiment. Shanks et al. argue that there are two modeling alternatives to represent part-whole relationships : an implicit representation and an explicit one. By conducting an experiment, they insist that the explicit representation increases the value of a conceptual model. Moreover, Shanks et al. justify their findings by citing the BWW ontology. Recently, the study from Shanks et al. faces criticism. Allen and March (2012) argue that Shanks et al.'s experiment is lack of validity and reliability since the experimental setting suffers from error-prone and self-defensive design. They point out that the experiment is intentionally fabricated to support the idea, as such that using concrete UML concepts results in positive results in understanding models. Additionally, Allen and March add that the experiment failed to consider boundary conditions; thus reducing credibility. Shanks and Weber (2012) contradict flatly the argument suggested by Allen and March (2012). To defend, they posit the BWW ontology is righteously applied in supporting the research. Moreover, the experiment, they insist, can be fairly acceptable. Therefore, Shanks and Weber argue that Allen and March distort the true value of Shanks et al. by pointing out minor limitations. In this study, we try to investigate the dispute around Shanks et al. in order to answer to the following question : What is the proper value of the study conducted by Shanks et al.? More profoundly, we question whether or not using the BWW ontology can be the only viable option of exploring better conceptual modeling methods and procedures. To understand key issues around the dispute, first we reviewed previous studies relating to the BWW ontology. We critically reviewed both of Shanks and Weber and Allen and March. With those findings, we further discuss theories on part-whole (or part-of) relationships that are rarely treated in the dispute. As a result, we found three additional evidences that are not sufficiently covered by the dispute. The main focus of the dispute is on the errors of experimental methods: Shanks et al. did not use Bunge's Ontology properly; the refutation of a paradigm shift is lack of concrete, logical rationale; the conceptualization on part-whole relations should be reformed. Conclusively, Allen and March indicate properly issues that weaken the value of Shanks et al. In general, their criticism is reasonable; however, they do not provide sufficient answers how to anchor future studies on part-whole relationships. We argue that the use of the BWW ontology should be rigorously evaluated by its original philosophical rationales surrounding part-whole existence. Moreover, conceptual modeling on the part-whole phenomena should be investigated with more plentiful lens of alternative theories. The criticism on Shanks et al. should not be regarded as a contradiction on evaluating modeling methods of alternative part-whole representations. To the contrary, it should be viewed as a call for research on usable and useful approaches to increase value of conceptual modeling.

Derivation of a GIS-based watershed-scale conceptual model for the St. Jones River Delaware from habitat-scale conceptual models

Looking backwards, it makes sense to discuss the value that Conceptual Modeling has provided to the Information Systems Design and Development area. Thinking about the present, the most advanced Software Engineering approaches oriented to producing quality software propose using extensively conceptual model-based approaches. Conceptual Modeling is widely used in the Information Systems domain. Nevertheless, in terms of Conceptual Model Evolution, we should wonder which new application domains will become more challenging for Conceptual Modeling in the very near future. In an attempt to answer that question, one path to follow is associated to the Bioinformatics domain and specifically, to confront the problem of precise understanding of the Human Genome. The problems related to this topic have become first-order issues in which, curiously, the role of Conceptual Modeling has not yet been fully exploited. The comprehension of the Human Genome is an extremely attractive topic for future research taking into account the continuous and increasing interest that is being generated. Therefore, it is worth analyzing how Conceptual Modeling principles, methods and techniques could help to face the problem and how Conceptual Modeling could aid to provide more efficient solutions. The basic goal of this talk will be the introduction and the discussion of these ideas. If we look at the Human Genome as the representation of some Conceptual Model ---which is not yet known-, interesting analogies with the modern Model-Driven Software Development principles appear. As a precise interpretation of the Human Genome would be much easier if the underlying model were known, Conceptual Modeling can provide new ways of facing that problem in order to obtain new and better strategies and solutions.

In model driven Enterprise Information Systems (EIS) conceptual ER models are fundamental. We demonstrate how a phenomenological modeling ontology for conceptual models assists in creating meaningful, auto generated EIS. Application of the phenomenological ontology on conceptual modeling is shown in a graphical, easy-to-use CASE tool, CoreWEB, which support both editing of models and generating user interfaces for consistent manipulation of instance data. A visual and easy-to-grasp demonstration of conceptual modeling with live generation of EIS should be a welcome holistic complement to specialized, focused, in-depth presentations. Audience at all levels find food for thought and inspiration to continue to explore conceptual modeling in general and phenomena oriented approaches with systems generation in particular.

ABSTRACTThis paper identifies and defines common relationships among video games and interactive media in an attempt to improve our understanding of and support conceptual data modeling in this domain. Existing models and standards such as Dublin Core, Functional Requirements for Bibliographic Records, Resource Description and Access, CIDOC Conceptual Reference Model, and Cataloging Cultural Objects do offer a variety of relationships between resources; however, many video games are related in more complex ways that cannot be adequately represented by these models and standards, especially in ways that reflect how game players understand games and how cultural heritage institutions might curate them. After thorough review of existing standards, consultation with domain experts, and examination of sample game sets, we identified and defined 11 relationship types and 4 grouping entities prevalent in the video game domain, and offer suggestions on how these might be represented in a conceptual model.

Objective: To develop a simple deterministic lumped conceptual rainfall-runoff model. Methods/statistical analysis: This study developed a model based on the water balance, in which the model tracks precipitation, simulated soil water content, surface runoff, evapotranspiration, percolation, and return streamflow. The major input requirements for this model are hourly rainfall, hourly potential evaporation and the average daily streamflow record. The model is calibrated and tested using the available data collected from only one semi-arid ephemeral catchment located in the central part of Jordan. The model was optimized using Shuffle Complex Evolution (SCE.UA) optimization method. The performance of the model was evaluated using the goodness of fit measures. Findings: The simulation results obtained show that the proposed model has a good match between observed and simulated streamflow for both optimization and validation stages. Application/improvements: The good simulation results obtained indicate that the model will be useful in design of hydraulic structures and for effective management of water resources at arid and semi-arid catchments. In order to strengthen the validation and applicability of the proposed model, empirical research needs to be conducted. Further research is required to assess the predictive uncertainty associated with model parameters and other inputs. Keywords: Hydrological Modeling, Rainfall-Runoff Relation, Streamflow Forecasting, Conceptual Model, Arid Catchments.