Modeling difficulties in creating conceptual data models

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

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.

Computer applications in non-traditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and the design of databases with imprecision and uncertainty. In this chapter, a different level of fuzziness based on fuzzy set and possibility distribution theory will be introduced into the IFO data model and the corresponding graphical representations will be given. The IFO data model is then extended to a fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.

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.

In the past few years, object–oriented (O–O) conceptual data modeling has emerged as an alternative to the traditional technique of entity–relationship modeling. O–O modeling is based on the premise that the resulting models are easier to use and understand. However, most claims of O–O modeling superiority are not empirically verified. Previous studies in this area have focused on a database modeler’s ability to create conceptual data models from a written description, but the concept of understanding a completed data model by a database designer has not been investigated. Thus, this study explores a database designer’s ability to understand an O–O conceptual data model – the Object Modeling Technique (OMT) – compared to an E–R model – the extended–entity– relationship model (EER). The OMT and EER conceptual data modeling techniques are compared using three modeling performance criteria: (1) model understanding; (2) time to understand; and (3) perceived ease–of–use. Results of this study indicate that the only difference between the two techniques is in the time to understand – OMT is significantly faster for both simple and complex problems.

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.

The human genome is traditionally represented as a DNA sequence of three billion base pairs. However, its intricacies are captured by many more complex signals, representing DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data formats is used to represent such signals. Different conceptual models explain such elaborate structure and behavior. Among them, the Conceptual Schema of the Human Genome (CSG) provides a concept-oriented, top-down representation of the genome behavior – independent of data formats. The Genomic Conceptual Model (GCM) instead provides a data-oriented, bottom-up representation, targeting a well-organized, unified description of these formats. We hereby propose to join these two approaches to achieve a more complete vision, linking (1) a concepts layer, describing genome elements and their conceptual connections, with (2) a data layer, describing datasets derived from genome sequencing with specific technologies. The link is established when specific genomic data types are chosen in the data layer, thereby triggering the selection of a view in the concepts layer. The benefit is mutual, as data records can be semantically described by high-level concepts and exploit their links. In turn, the continuously evolving abstract model can be extended thanks to the input provided by real datasets. As a result, it will be possible to express queries that employ a holistic conceptual perspective on the genome, directly translated onto data-oriented terms and organization. The approach is here exemplified using the DNA variation data type but is applicable to all genomic information.KeywordsConceptual modelingBiological datasetsGenomics

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.

Defining and validating metrics for assessing the understandability of entity–relationship diagrams

Abstract. A new version of the Land Administration Domain Model (LADM) has been discussed and is under further development in ISO/TC 211 on Geographic Information. One of the extending parts is where the model can accommodate complex and advanced marine properties and cadastral objects. Currently, the fundamentals part of this new version (LADM Edition II) has been examined by the committee, and a few elements need to be considered, especially for marine space georegulation. Based on the possibility of embedding LADM with marine cadastre as agreed by several researchers, the concept of marine cadastre data model within land administration context has been anticipated in many countries (e.g., Canada, Greece, Turkey, Australia, and Malaysia). Part of the research focused on constructing and developing the appropriate data models to manage marine spaces and resources most effectively. Several studies have attempted to establish a conceptual model for marine cadastre in Malaysia. However, there is still no acceptable marine data model. Thus, this paper proposed a marine data model for Malaysia based on the international standard, LADM. The approach, by definition, can be applied to the marine environment in terms of controlling and modelling a variety of rights, responsibilities, and restrictions. The Unified Modelling Language (UML) application was utilized to construct the conceptual and technical models via Enterprise Architect as part of the validation process. The data model was constructed within the marine's concept in Malaysia to meet international standards. The features of the data model were also discussed in the FIG workshop (9th LADM International Workshop 2021). The experiment on the data model also includes 3D visualization and simple query.

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.

Fuzzy set theory has been extensively applied to extend various database models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) data models. This paper reviews fuzzy conceptual data models proposed in the literature, where fuzzy ER/EER, IFO and UML data models are mainly discussed, and reviews the applications of fuzzy conceptual data models.

PurposeWeb applications are subject to continuous changes and rapid evolution triggered by increasing competition, especially in commercial domains such as electronic commerce. Unfortunately, usually they are implemented without producing any useful documentation for subsequent maintenance and evolution. Thereof, the maintenance of such systems becomes a challenging problem as the complexity of the web application grows. Reverse engineering has been heralded as one of the most promising technologies to support effective web application maintenance. This paper aims to present a reverse engineering approach that helps understanding existing undocumented web applications to be maintained or evolved.Design/methodology/approachThe proposed approach provides reverse engineering rules to generate a conceptual schema from a given domain ontology by using a set of transformation rules. The reverse engineering process consists of four phases: extracting useful information; identifying a set of ontological constructs representing the concepts of interest; enriching the identified set by additional constructs; and finally deriving a conceptual schema.FindingsThe advantage of using ontology for conceptual data modeling is the reusability of domain knowledge. As a result, the conceptual data model will be made faster, easier and with fewer errors than creating it in usual way. Designers can use the extracted conceptual schema to gain a better understanding of web applications and to assist in their maintenance.Originality/valueThe strong point of this approach is that it relies on a very rich semantic reference that is domain ontology. However, it is not possible to make a straightforward transformation of all elements from a domain ontology into a conceptual data model because ontology is semantically richer than data conceptual models.

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.