Observations on the ODMG-93 proposal for an object-oriented database language

Deductive object-oriented databases are intended to integrate the deductive and object-oriented database techniques to combine the best of two approaches and to overcome their inherent shortcomings, with a number of deductive object-oriented database languages proposed. However, most of these languages are only structurally object-oriented. Important behaviorally object-oriented features such as methods and encapsulation common in object-oriented database systems are not properly supported. This chapter presents a novel deductive object-oriented database language called ROL2 that extends structurally object-oriented database language ROL with all behaviorally object-oriented features. It supports in a rule-based framework all important object-oriented features such as object identity, complex objects, typing, information hiding, rule-based methods, encapsulation of such methods, overloading, late binding, polymorphism, class hierarchies, multiple structural and behavioral inheritance with overriding, blocking, and conflict handling. It is so far the only deductive object-oriented database language that supports all these features in a uniform rule-based framework.

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FRIL++ and Its Applications

Advances in Fuzzy Object-Oriented Databases ◽

10.4018/978-1-59140-384-5.ch004 ◽

2011 ◽

pp. 113-152 ◽

Cited By ~ 1

Author(s):

Jonathan Michael Rossiter ◽

Tru Hoang Cao

Keyword(s):

Machine Learning ◽

Object Oriented ◽

Upper Bounds ◽

Default Reasoning ◽

Probabilistic Logic ◽

Database Language ◽

Basic Features ◽

Property Inheritance ◽

Logic Rule ◽

Object Oriented Database

We introduce a deductive probabilistic and fuzzy object-oriented database language, called FRIL++, which can deal with both probability and fuzziness. Its foundation is a logic-based probabilistic and fuzzy object-oriented model where a class property (i.e., an attribute or a method) can contain fuzzy set values, and uncertain class membership and property applicability are measured by lower and upper bounds on probability. Each uncertainly applicable property is interpreted as a default probabilistic logic rule, which is defeasible, and probabilistic default reasoning on fuzzy events is proposed for uncertain property inheritance and class recognition. The design, implementation, and basic features of FRIL++ are presented. FRIL++ can be used as both a modeling and a programming language, as demonstrated by its applications to machine learning, user modeling, and modeling with words herein.

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