Behaviorally adaptive objects

Lang, Stefan M.; Lockemann, Peter C.

doi:10.1002/(sici)1096-9942(1998)4:3<169::aid-tapo3>3.0.co;2-n

Cited by 4 publications

(2 citation statements)

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“…Some modeling approaches and systems support object adaptation or multiple classification in a nonhierarchical context. For example, Lang and Lockemann [1998] propose a model in which an object infers the context in which it is operating and adapts its behavior as appropriate to that context. Their proposal focuses on behavior-objects are still assumed to have "situation-independent properties" which amount to prespecified classification.…”

Section: Solutions To the Problems Of Inherent Classificationmentioning

confidence: 99%

“…In this respect, the approach is similar to two others. CLASSIC is a knowledge representation language which allows instances to be added to a knowledge base without reference to their properties, or with partial information, and which also supports inferences about class membership based on properties an instance possesses [Borgida et al 1989] Likewise, OPAQUE is a model in which objects can dynamically modify their behavior based on the environment in which they are operating [Lang and Lockemann 1998]. …”

Section: Some Practical Considerationsmentioning

confidence: 99%

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Emancipating instances from the tyranny of classes in information modeling

Parsons¹,

Wand

2000

ACM Trans. Database Syst.

126

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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 twolayered 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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Section: Solutions To the Problems Of Inherent Classificationmentioning

confidence: 99%

Section: Some Practical Considerationsmentioning

confidence: 99%