面向事实的概念建模方法研究综述

领域概念知识建模是构建信息系统分析的关键技术和任务之一,同时也是知识工程的瓶颈问题,其难点在于如何正确完整地捕捉和验证领域专家的知识。ER和UML等有属性特性的建模方法能够很好地描述领域知识,但却难以让领域专家确认知识的正确性和完整性。面向事实的信息建模(FOM)是一种完全面向自然语言交流的领域概念知识建模方法,是一种理想的概念建模和本体工程辅助工具。简要分析了概念建模过程,比较了不同概念建模方法,介绍了FOM的技术演化过程,从业务规则、动态建模、模型抽象机制、模型转换和工程应用等方面评述了FOM的研究现状和待研究问题。     

Using a trope-based foundational ontology for bridging different areas of concern in ontology-driven conceptual modeling

In recent years, ontology-driven reference models have gained much attention in the literature due to their potential key role in activities such as complex information modeling and semantic interoperability. The engineering process of these conceptual models should account for different phases addressing different areas of concern. In an initial phase of conceptual domain modeling, the target modeling artifacts should be constructed with the goal of maximizing quality attributes such as expressivity and truthfulness to the represented domain in reality. In a subsequent development phase, the resulting domain models can be used to guide the design decisions in the construction of different implementation artifacts addressing different computational concerns. In this paper, we present a philosophically sound, cognitively-oriented and formally characterized foundational theory of objects and tropes (property-instances). Moreover, we use this theory to bring about engineering contributions to both the aforementioned phases of ontology-driven conceptual modeling. Firstly, we show how this theory has been used to (re)design a system of modeling primitives underlying the conceptual domain modeling language OntoUML. Furthermore, we provide precise directives on how to map conceptual domain models in this language to their implementation in less-expressive computationally-oriented codification languages. In particular, we address here a mapping strategy to OWL (Web Ontology Language) that partially preserves the modal-temporal semantics of OntoUML. Finally, we discuss computational support for the proposed approach in terms of conceptual model construction, automatic transformation and temporal querying.     

What do the pictures mean? Guidelines for experimental evaluation of representation fidelity in diagrammatical conceptual modeling techniques

It is important to articulate the objectives and underlying assumptions behind a growing body of experimental research in conceptual modeling. We provide four guidelines for developing materials for experiments that evaluate conceptual modeling techniques, under the assumption that a primary purpose of conceptual modeling is to facilitate communication between analysts and users in validating domain knowledge during systems development. These guidelines assist in developing experimental materials that support meaningful tests of domain semantics. We present empirical evidence indicating the value of two of the guidelines. We also evaluate selected recent experiments on conceptual modeling with respect to the guidelines.     

Activity labeling in process modeling: Empirical insights and recommendations

Few studies have investigated the factors contributing to the successful practice of process modeling. In particular, studies that contribute to the act of developing process models that facilitate communication and understanding are scarce. Although the value of process models is not only dependent on the choice of graphical constructs but also on their annotation with textual labels, there has been hardly any work on the quality of these labels. Accordingly, the research presented in this paper examines activity labeling practices in process modeling. Based on empirical data from process modeling practice, we identify and discuss different labeling styles and their use in process modeling praxis. We perform a grammatical analysis of these styles and use data from an experiment with process modelers to examine a range of hypotheses about the usability of the different styles. Based on our findings, we suggest specific programs of research towards better tool support for labeling practices. Our work contributes to the emerging stream of research investigating the practice of process modeling and thereby contributes to the overall body of knowledge about conceptual modeling quality.     

Applying ontology-based rules to conceptual modeling: a reflection on modeling decision making

Conceptual modeling represents a domain independently of implementation considerations for purposes of understanding the problem at hand and communicating about it. However, different people may construct different models given the same domain. Variations among correct models, while known and familiar in practice, have hardly been investigated in the literature. Their roots are in the decisions made during the modeling process, where modelers face the need to map reality into modeling constructs. This paper reports an empirical study whose aim is to explore model variations and in particular to examine possible directions for reducing them. Specifically, the study uses a multimethod research paradigm to examine the effect of applying ontology-based modeling rules on modeling decisions as reflected in resulting model variations. The findings of the study provide insights into the variations phenomenon, as well as to the application of ontology-based modeling rules.     

A framework for empirical evaluation of conceptual modeling techniques

The paper presents a framework for the empirical evaluation of conceptual modeling techniques used in requirements engineering. The framework is based on the notion that modeling techniques should be compared via their underlying grammars. The framework identifies two types of dimensions in empirical comparisons—affecting and affected dimensions. The affecting dimensions provide guidance for task definition, independent variables and controls, while the affected dimensions define the possible mediating variables and dependent variables. In particular, the framework addresses the dependence between the modeling task—model creation and model interpretation—and the performance measures of the modeling grammar. The utility of the framework is demonstrated by using it to categorize existing work on evaluating modeling techniques. The paper also discusses theoretical foundations that can guide hypothesis generation and measurement of variables. Finally, the paper addresses possible levels for categorical variables and ways to measure interval variables, especially the grammar performance measures.     

Recommendation-based editor for business process modeling

To ensure proper and efficient modeling of business processes, it is important to support users of process editors adequately. With only minimal modeling support, the productivity of novice business process modelers may be low when starting process modeling. In this article, we present a theoretically sound and empirically validated recommendation-based modeling support system, which covers different aspects of business process modeling. We consider basic functionality, such as an intuitive search interface, as well as advanced concepts like patterns observed in other users' preferences. Additionally, we propose a multitude of interaction possibilities with the recommendation system, e.g., different metrics that can be used in isolation or an overall recommender component that combines several sub metrics into one comprehensive score. We validate a prototype implementation of the recommendation system with exhaustive user experiments based on real-life process models. To our knowledge, this is the only comprehensive recommendation system for business process modeling that is available.     

Improving the quality of conceptual modeling using cognitive mapping techniques

Conceptual modeling involves the understanding and communication between system analysts and end-users. Many factors may affect the quality of conceptual modeling processes as well as the models per se. Human cognition plays a pivotal role in understanding these factors and cognitive mapping techniques are effective tools to elicit and represent human cognition. In this paper, we look at the use of cognitive mapping techniques to improve the quality of conceptual modeling. We review frameworks on quality in conceptual modeling and examine the role of human cognition in conceptual modeling. The paper also discusses how human cognition is related to quality in conceptual modeling, the various cognitive mapping techniques, and how these cognitive mapping techniques can be used in conceptual modeling. Through a case study, the paper describes ways of incorporating cognitive mapping techniques to a popular systems development methodology—Soft Systems Methodology—to improve the quality of conceptual modeling.     

虚拟环境概念模型建模方法研究*

由于目前常用的概念建模方法对于虚拟环境中物体方位、行为和交互模型等信息缺乏足够的描述方法,在吸取现有概念模型建模概念和技术的基础上,提出了虚拟环境概念模型建模框架,给出了虚拟场景静态结构和动态行为的概念建模方法,并通过虚拟化学实验平台的设计和实现验证了方法的可行性。通过在虚拟环境设计阶段引入概念建模阶段,使不具备专业编程知识的普通人员通过使用图形化的建模语言也能够进行虚拟环境的开发,为虚拟环境的顶层设计提供了一个科学、有效的手段。     

Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

Previous studies on learning challenges in the field of modeling focus on cognitive perspectives, such as model understanding, modeling language knowledge and perceptual properties of graphical notation by novice business analysts as major sources affecting model quality. In the educational context outcome feedback is usually applied to improve learning achievements. However, not many research publications have been written observing the characteristics of a modeling process itself that can be associated with better/worse learning outcomes, nor have any empirically validated results been reported on the observations of modeling activities in the educational context. This paper attempts to cover this gap for conceptual modeling. We analyze modeling behavior (conceptual modeling event data of 20 cases, 10.000 events in total) using experimental logging functionality of the JMermaid modeling tool and process mining techniques. The outcomes of the work include modeling patterns that are indicative for worse/better learning performance. The results contribute to (1) improving teaching guidance for conceptual modeling targeted at process-oriented feedback, (2) providing recommendations on the type of data that can be useful in observing a modeling behavior from the perspective of learning outcomes. In addition, the study provides first insights for learning analytics research in the domain of conceptual modeling.     

A knowledge-based object modeling advisor for developing quality object models

Object models or class diagrams are widely used for capturing information system requirements in terms of classes with attributes and operations, and relationships among those classes. Although numerous guidelines are available for object modeling as part of requirements modeling, developing quality object models has always been considered a challenging task, especially for novice systems analysts in business environments. This paper presents an approach that can be used to support the development of quality object models. The approach is implemented as a knowledge-based system extension to an open source CASE tool to offer recommendations for improving the quality of object models. The knowledge component of this system incorporates an ontology of quality problems that is based on a conceptual model quality framework commonly found in object models, the findings of related empirical studies, and a set of analysis patterns. The results obtained from an empirical evaluation of the prototype demonstrate the utility of this system, especially with respect to recommendations related to the model completeness aspect of semantic quality.     

Modeling events as entities in object-oriented conceptual modeling languages

Most current conceptual modeling languages and methods do not model events as entities. We argue that, at least in object-oriented (O-O) languages, modeling events as entities provides substantial benefits. We show that a method for behavioral modeling that deals with event and entity types in a uniform way may yield better behavioral schemas. The proposed method makes extensive use of language constructs such as constraints, derived types, derivation rules, type specializations and operations, which are present in all complete O-O conceptual modeling languages. The method can be adapted to most O-O languages. In this paper we explain its adaptation to the UML.     

A conceptual modeling framework for discrete event simulation using hierarchical control structures

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.     

On the ontological expressiveness of conceptual modeling grammars for service productivity management

Conceptualizing, analyzing, and optimizing service productivity is imperative to build up and to sustain competitive advantage in today’s global service economy. However, service productivity is yet to be thoroughly conceptualized and supported by IT artifacts so as to design, compute, and interpret meaningful productivity models. The purpose of this study is to evaluate the ontological expressiveness of conceptual modeling grammars for service productivity management. Due to a lack of a complete ontology for service productivity management, we compile evaluation criteria by reviewing authoritative theory. Against these criteria, a selection of conceptual modeling grammars is analyzed by reviewing the grammars’ meta models for completeness. The analysis contributes two major insights. First, theory in productivity management appears equivocal and is too unspecific to guide the design of IT artifacts. Second, conceptual modeling grammars are subject to various ontological deficiencies with respect to service productivity management. Two core implications are identified. First, service productivity theories need to be refined as design theories in order to comprehensively inform the design of IT artifacts. Second, conceptual modeling grammars must be extended and aligned with each other.     

How do practitioners use conceptual modeling in practice?

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether practitioners still embraced conceptual modeling seriously. In addition, what are the most popular techniques and tools used for conceptual modeling? What are the major purposes for which conceptual modeling is used? The study found that the top six most frequently used modeling techniques and methods were ER diagramming, data flow diagramming, systems flowcharting, workflow modeling, UML, and structured charts. Modeling technique use was found to decrease significantly from smaller to medium-sized organizations, but then to increase significantly in larger organizations (proxying for large, complex projects). Technique use was also found to significantly follow an inverted U-shaped curve, contrary to some prior explanations. Additionally, an important contribution of this study was the identification of the factors that uniquely influence the decision of analysts to continue to use modeling, viz., communication (using diagrams) to/from stakeholders, internal knowledge (lack of) of techniques, user expectations management, understanding models’ integration into the business, and tool/software deficiencies. The highest ranked purposes for which modeling was undertaken were database design and management, business process documentation, business process improvement, and software development.     

基于Petri网的机电产品概念设计功能模型研究

功能分析是概念设计中最重要的阶段,从黑箱法的理论出发,以流及其状态和功能为基本要素建立了基于Petri网的机电一体化产品概念设计功能模型,为产品概念设计过程的优化以及设计效率的提高打下了良好基础,为以后智能软件的开发提供了良好的模型.     

A survey of sketch based modeling systems

As 3D technology, including computer graphics, virtual reality and 3D printing, has been rapidly developed in the past years, 3D models are gaining an increasingly huge demand. Traditional 3D modeling platforms such as Maya and ZBrush, utilize “windows, icons, menus, pointers” (WIMP) interface paradigms for fine-grained control to construct detailed models. However, the modeling progress can be tedious and frustrating and thus too hard for a novice user or even a well trained artist. Therefore, a more intuitive interface is needed. Sketch, an intuitive communication and modeling tool for human beings, becomes the first choice of modeling community. So far, various sketch-based modeling systems have been created and studied. In this paper, we attempt to show how these systems work and give a comprehensive survey.We review and categorize the systems in four aspects: the input, the knowledge they use, the modeling approach and the output. We also discuss about inherent challenges and open problems for researchers in the future.     

Understanding quality in conceptual modeling

With the increasing focus on early development as a major factor in determining overall quality, many researchers are trying to define what makes a good conceptual model. However, existing frameworks often do little more than list desirable properties. The authors examine attempts to define quality as it relates to conceptual models and propose their own framework, which includes a systematic approach to identifying quality-improvement goals and the means to achieve them. The framework has two unique features: it distinguishes between goals and means by separating what you are trying to achieve in conceptual modeling from how to achieve it (it has been made so that the goals are more realistic by introducing the notion of feasibility); and it is closely linked to linguistic concepts because modeling is essentially making statements in some language