Jeffrey Parsons
Abstract
Database design commonly assumes, explicitly or implicitly, that instances must belong to classes. This can be termed the assumption of inherent classification. We argue that the extent and complexity of problems in schema integration, schema evolution, and interoperability are, to a large degree, consequences of inherent classification. Furthermore, we make the case that the assumption of inherent classification violates philosophical and cognitive guidelines on classification and is, therefore, inappropriate in view of the role of data modeling in representing knowledge about application domains.
As an alternative, we propose a layered approach to modeling in which information about instances is separated from any particular classification. Two data modeling layers are proposed: (1) an instance model consisting of an instance base (i.e., information about instances and properties) and operations to populate, use, and maintain it; and (2) a class model consisting of a class base (i.e., information about classes defined in terms of properties) and operations to populate, use, and maintain it. The two-layered model provides class independence. This is analogous to the arguments of data independence offered by the relational model in comparison to hierarchical and network models. We show that a two-layered approach yields several advantages. In particular, schema integration is shown to be partially an artifact of inherent classification that can be greatly simplified in designing a database based on a layered model; schema evolution is supported without the complexity of operations currently required by class-based models; and the difficulties associated with interoperability among heterogeneous databases are reduced because there is no need to agree on the semantics of classes among independent databases. We conclude by considering the adequacy of a two-layered approach, outlining possible implementation strategies, and drawing attention to some practical considerations.
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Index Terms
Emancipating instances from the tyranny of classes in information modeling
Reviews
Jaroslav Pokorny
With the advent of integrated heterogeneous information sources, there is a renewed interest in information modeling (IM). The authors emphasize that most of the previous approaches to IM are based on the assumption of inherent classification, that is, the first modeling step is usually to identify the classes or types of things needed to describe the domain. The goal of the paper is to show that this assumption is inappropriate in many tasks of IM. The authors propose a two-layered approach to IM. The first layer represents the existence of things with properties, independent of the classes to which the things belong. The second layer consists of class definitions based on sets of properties. In Section 2, some undesirable consequences of inherent classification are discussed. On the schema design level, the authors explain problems of multiple classification, view integration, schema evolution, and interoperability. Other problems arise in database operation. They include handling exceptional instances, reclassifying instances, adding and removing instances, and removing and redefining a class. Section 3 attempts to describe the theoretical foundations of classifications, with the help of ontology and epistemology. The authors use Bunge's simple ontological model, which dates to the 1970s. They conclude that both ontology and classification theory recognize that instances exist independent of any classes. The authors adapt these ideas in a two-layered approach in Section 4. They draw two partial conclusions: recognizing the existence of things should precede classifying them, and there is no single “correct” set of classes to model a given domain of instances and properties. The particular choice of classes (a view) depends on the application. The two-layered approach consists of the instance model and class model. In the former, instances and properties are represented. Instances can be queried and modified by primitive operations. Similar operations are designed for the class model. In Section 5 the authors present various implications of the two-layered model. They show that many problems connected with the assumption of inherent classification disappear in this approach. Section 6 discusses how to combine this approach with other models, particularly with the entity-relationship (ER) model and object models. Section 7 addresses some issues related to the implementation, efficiency, and practicality of the model. The most interesting part concerns querying a database. The authors present queries that are not available in traditional class-based databases, such as relational databases. For example, we can obtain identifiers of all read cars stored in a database, regardless of the database columns in which they are placed. The chapter concludes with a detailed discussion of how to implement the two-layered approach. The rest of the paper summarizes the main ideas of the model, points out its advantages, and gives a context for its application. Obviously, many other classical database issues can be reformulated and solved in this new paradigm. The paper is well-arranged and sound. It offers an interesting approach to IM, which can make many contributions and has many consequences for the development of future database architectures.
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