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Choosing classes in conceptual modeling
ion from Instances. A class can be defined only if there are instances in the relevant universe possessing all properties defining the class. Adherence to this principle supports the fundamental meaning of concept—a specification of the properties common to some instances. If no instances from the relevant universe share a specified set of properties, there is no “sameness” and no economy of representation. It would be cognitively wasteful to store such a set of properties as a concept, as it tells us nothing about the relevant universe. This principle is perhaps obvious for knowledge acquisition methods that build up abstract descriptions from a given set of objects. However, it is less obvious for methods based on inferring new categories from existing ones [5, 12], as these methods do not use instances to identify candidate classes. Moreover, abstraction from instances does not appear as an explicit requirement in object-oriented design and semantic data modeling. COMMUNICATIONS OF THE ACM June 1997/Vol. 40, No. 6 65 Maximal Abstraction. A relevant property possessed by all instances of a class should be included in the class definition. This principle supports both cognitive economy and inference. If certain properties shared by all instances are not part of the class (concept) definition, some similarities among the instances in the class are lost. Moreover, properties shared by all instances, but not included in the classification, cannot be inferred if an instance is identified as a member of the class. Abstraction from instances and maximal abstraction are requirements for a class to effectively express what the instances in a set have in common. We therefore propose that these principles be used as necessary requirements for a set of properties to be considered a class. We consider a set of properties a potential class in a relevant universe if and only if: • It has a nonempty extension (at some point in time); and • It contains all properties common to all instances in the extension. Note that although the set of properties forming a potential class is fixed, the properties possessed by an instance may change with time. As an instance acquires or loses properties, it might “move” in and out of a potential class. In other words, the population of a class might vary with time. It follows from the definition that two distinct potential classes cannot be defined by the same set of properties. In practice, if distinct names have been attached to identical sets of properties, these names are synonyms. Also, two distinct potential classes cannot have the same extension, that is, at every time, there is at least one instance in one class that is not included in the other. The implication of the second observation can be illustrated by an example. Consider a university department having a policy that only graduate students can serve as research assistants and that all of them must do so. Although “graduate student” and “research assistant” may reasonably be thought of as distinct classes, the fact that they must always have the same instances means they cannot constitute distinct potential classes. Even though different properties could be chosen to define graduate student and research assistant, the same instances must share these properties. Maintaining the two as distinct classes is redundant. Moreover, it might lead to misunderstandings, since for many purposes people think of “graduate student” and “research assistant” as different concepts. For instance, a user in the campus health center may reasonably distinguish graduate student and research assistant classes, believing instances of each carry different forms of health insurance. This example shows how examining class extensions can help avoid misconceptions arising from prior informal “conceptual baggage.” The analysis in this example required that the extension of the classes be known. In general, to prevent redundancy, we propose that classification strategies check whether different classes have identical extensions. This does not imply a need for complete enumeration of instances, which, in most cases, may be impractical. Instead, it may be possible to identify synonymous classes by some reasoning. For example, a student organization may use the concept “member.” If all students of a university are members of this organization and only students are members, the concept “member” can be identified as synonymous with “student” without examining the actual instances. Class Structures The potential class concept restricts the possible classes formed to model the relevant universe. Whether a given collection of properties is a potential class can be determined irrespective of the other classes being considered. However, in conceptual modeling we are interested in collections of classes (or concepts). We now turn to the implications of applying cognitive economy and inference to such collections. Inference relates to the ability to infer properties from class membership. Cognitive economy involves balancing the objective of maximizing information content with that of minimizing the number of stored concepts, by emphasizing the value of distinguishing between concepts whose instances possess “meaningful differences.” Thus, the full information about an instance is usually distributed among all classes to which it belongs. To eliminate loss of information and minimize redundancy, we propose two additional principles that apply to collections of classes: completeness, which requires that all properties from the relevant universe be used in a classification, and nonredundancy, which ensures there are no redundant classes. Completeness. Given a relevant universe of instances and properties, every property should be used in the definition of at least one class in the set of classes. If a property is part of the relevant universe, instances possessing it have “meaningful differences” 66 June 1997/Vol. 40, No. 6 COMMUNICATIONS OF THE ACM with respect to those that do not. By requiring all relevant properties to be part of the definition of some class, the principle of completeness supports the “maximum information” aspect of cognitive economy. The principle also supports inference, as it ensures every relevant property can be inferred by considering all classes to which the instance belongs. However, cognitive economy also requires “least cognitive effort,” leading to the next requirement. Nonredundancy. A class that is a subclass of several other classes should be defined by at least one property not in any of its superclasses. Suppose that, in violation of this requirement, a new class is defined by all the properties of some existing classes. Every instance of the new class is an instance of all of the original classes. If there are no additional properties, the new class adds no information about its instances. Therefore, the principle of nonredundancy supports the “minimize the number of classes” aspect of cognitive economy. We define class structure as a set of potential classes satisfying the principles of completeness and nonredundancy; that is: • Every property in the relevant universe appears in the definition of at least one class; and • No class is defined only in terms of the properties of a set of other classes. To have any practical consequence, it must be possible to construct a class structure for any given relevant universe. It can be proven by construction that for every relevant universe a class structure can be formed. Specialization of classes is widely used in both knowledge representation (where it is frequently called subsumption [2, 12]) and in object-oriented design and data modeling [11]. In our propertybased (intensional) view of classes, we defined specialization as forming one class from another class by adding properties. A direct consequence of the observation that distinct potential classes cannot have the same extension is that the instances of a subclass are a strict subset of those of every superclass and therefore are also instances of the latter. The second condition in the definition of class structure also implies that, in a class structure, a specialized class must have properties not possessed by any of its superclasses. To see the intuitive meaning of this, consider again the university example, except that now a research assistant may or may not be a student. If “research assistants who are students” is useful for modeling this domain, we contend there must be properties of this population in addition to those of students and of research assistants, such as a special tuition fee. From a cognitive perspective, without such properties there is no need to define a new class. Properties of Class Structures Class structures have a number of interesting and useful properties for conceptual modeling. First, there will generally be a “pluralism of views” supporting the cognitive principles. This is manifested by two observations: Multiplicity. More than one class structure may exist for a relevant universe having more than one property. Multiplicity implies that several views of a domain, each supporting cognitive economy and inference, can coexist. Inclusion. Every potential class can be included in some class structure. Inclusion ensures that every concept of interest can, in principle, be included in a model that supports cognitive principles. A second useful property of class structures is that they can be made fully descriptive of all instances in the relevant universe. A class structure is required to include all relevant properties. Yet it is possible for a given instance that not all its relevant properties are included in the classes to which it belongs. Also, it is possible for an instance in the relevant universe to be unclassified. In particular, when a class is removed from a class structure, some of its instances may no longer belong to any class. In terms of classification theory, these possibilities reflect
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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.
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An Ontological Metamodel of Classifiers and Its Application to Conceptual Modelling and Database Design
Classification is a key concept in conceptual modeling. However, the recognition that there might be different kinds of classes has received limited attention in conceptual modeling research. Most work has focused on the concept of role. However, confusion exists on the definition, properties, and representation of role. In this paper, we propose a metamodel of classifiers based on ontological foundations. We focus on the notion of role in the object-oriented and conceptual modeling literature. The metamodel conforms to the fundamental role features identified in the literature and handles several problems associated with role modeling. Furthermore, we contrast conceptual models created using our metamodel to those created using traditional ER or class modeling approaches, and highlight the database design implications of the differences. Using examples, we demonstrate that relational database schemata generated using our approach are more stable with respect to changing requirements.
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Experimental Research on Conceptual Modeling: What Should We Be Doing and Why?
This panel considers a number of contentious issues in the conduct of experimental research on conceptual modeling. The panelists will present a range of perspectives on the issues to encourage audience input and discussion.
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Property-Based Semantic Reconciliation of Heterogeneous Information Sources
Integrating information from diverse sources is of great importance in the database area. The main difficulty in information integration is reconciling data semantics. Common approaches to semantic reconciliation are based on first identifying similar entity types in various sources, and then reconciling entity type properties (attributes and relationships). Such approaches assume all instances to be reconciled belong to well-defined types. We suggest an alternative approach based on two fundamental principles. First, reconciliation does not require that instances be assigned to specific types. Instead, sources can be reconciled by analyzing similarities of properties. Second, properties that appear different may be manifestations of a higher-level property that has the same meaning across sources. We present the fundamental ideas underlying our approach, analyze its potential advantages, suggest how the approach can be formalized, demonstrate with examples the feasibility of using it for semantic reconciliation, and suggest directions for further research.
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Dimensions of UML Diagram Use: A Survey of Practitioners
The UML is an industry standard for object-oriented software engineering. However, there is little empirical evidence on how UML is used. This article reports results of a survey of UML practitioners. We found differences in several dimensions of UML diagram usage on software development projects including; frequency, the purposes for which they were used, and the roles of clients/users in their creation and approval. System developers are often ignoring the â€œuse case-drivenâ€ prescription that permeates much of the UML literature, making limited or no use of either use case diagrams or textual use case descriptions. Implications and areas requiring further investigation are discussed.
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Model driven middleware: A new paradigm for developing distributed real-time and embedded systems
Distributed real-time and embedded (DRE) systems have become critical in domains such as avionics (e.g., flight mission computers), telecommunications (e.g., wireless phone services), tele-medicine (e.g., robotic surgery), and defense applications (e.g., total ship computing environments). These types of system are increasingly interconnected via wireless and wireline networks to form systems of systems. A challenging requirement for these DRE systems involves supporting a diverse set of quality of service (QoS) properties, such as predictable latency/jitter, throughput guarantees, scalability, 24x7 availability, dependability, and security that must be satisfied simultaneously in real-time. Although increasing portions of DRE systems are based on QoS-enabled commercial-off-the-shelf (COTS) hardware and software components, the complexity of managing long lifecycles (often ~15-30 years) remains a key challenge for DRE developers and system integrators. For example, substantial time and effort is spent retrofitting DRE applications when the underlying COTS technology infrastructure changes. This paper provides two contributions that help improve the development, validation, and integration of DRE systems throughout their lifecycles. First, we illustrate the challenges in creating and deploying QoS-enabled component middleware-based DRE applications and describe our approach to resolving these challenges based on a new software paradigm called Model Driven Middleware (MDM), which combines model-based software development techniques with QoS-enabled component middleware to address key challenges faced by developers of DRE systems - particularly composition, integration, and assured QoS for end-to-end operations. Second, we describe the structure and functionality of CoSMIC (Component Synthesis using Model Integrated Computing), which is an MDM toolsuite that addresses key DRE application and middleware lifecycle challenges, including partitioning the components to use distributed resources effectively, validating software configurations, assuring multiple simultaneous QoS properties in real-time, and safeguarding against rapidly changing technology.