Data Abstraction Mechanisms in Sina/st

This paper describes a new data abstraction mechanism in an object-oriented model of computing. The data abstraction mechanism described here has been devised in the context of the design of Sina/st language. In Sina/st no language constructs have been adopted for specifying inheritance or delegation, but rather, we introduce simpler mechanisms that can support a wide range of code sharing strategies without selecting one among them as a language feature. Sina/st also provides a stronger data encapsulation than most of the existing object-oriented languages. This language has been implemented on the SUN 3 workstation using Smalltalk

Grand tour of concepts for object-orientation from a database point of view

Over the last few years, object-orientation has gained more and more importance within several disciplines of computer science (e.g. programming languages, knowledge engineering, and database systems). Numerous papers have defined one or another of its underlying concepts (sometimes in quite different ways), and some systems have been developed following those heterogeneous definitions. Nevertheless, papers investigating the dependencies and degrees of freedom of these concepts are rarely found. For this reason, the goal of this paper is not to add yet another definition of object-oriented concepts, but to identify existing relationships among these basic concepts that allow one to cover and classify various conceivable combinations of these conceptual building blocks. Dependencies, orthogonalities, and relations among concepts like object identity, encapsulation, classification, generalization, inheritance, etc. are revealed, showing numerous ways to compose different shades of object-orientation. This leads to alternatives encountered when constructing object-oriented systems, which are illustrated by classifying some well-known systems and prototypes from different areas. However, it is not our purpose to analyze the relative importance of these concepts. Instead, we investigate the concepts from a neutral point of view, presenting (but not evaluating) several degrees of object-orientation

Semantic networks

AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies

Personalizing Online Trading Without Spoiling Its Intrinsic Advantages

Although access to the Internet has become more efficient than before, investors seem still shy away from taking advantage of online trading. Early research indicates that one main reason for the setback is the impersonality of online trading. Brokerage firms and researchers have investigated various kinds of decision support that mimic offline brokerage services, but the lack of timeliness and simplicity of such support may have just ruined the purpose of online trading. To cope with the situation, we have investigated a heuristic model for representing financial securities such that inexperienced investors can naturally perceive personalized guidance for their investment decisions. In addition to a description of our model, this paper also discusses the reasoning schema behind the model and proposes the methods for constructing the model. In contrast to other approaches, our model is aimed to provide personalized decision support without spoiling the intrinsic advantages of online trading

Modular language processors as framework completions

Journal ArticleThe conceptual and specificational power of denotational semantics for programming language design has been amply demonstrated. We report here on a language implementation method that is similarly semantically motivated, but is based upon object-oriented design principles, and results in flexible and evolvable language processors. We apply this technique to the area of object-oriented (O-O) languages, in the form of a general metalevel architecture for objects and inheritance that facilitates the development of compilers and interpreters for 0-0 languages. This development strategy maintains architectural modularity by mapping conceptual language design decisions to isolatable parts of resulting language processors. Our architecture, which is presented as an OO framework, is characterized by (i) support for a broad set of modularity features including encapsulation and strong typing, and (ii) an "unbundled" view of inheritance, semantic features of which are decomposed by means of a set of module combination operations (combinators). We describe an implementation of our framework in C++, and assess its utility by constructing a compiler for a simple 0 - 0 extension to the programming language C. We further argue the flexibility of the resulting processor by outlining the incorporation of several significant extensions to the basic module language. We claim that the use of such a framework for compiler construction has many advantages, including a systematic language development method, processor software reuse, language extensibility, and potential for interoperability among languages.

Object-oriented data modeling

The object-oriented paradigm models local behavior, and to a lesser extent, the structure of a problem. Semantic data models describe structure and semantics. This thesis unifies the behavioral focus of the object-oriented paradigm with the structural and semantic focus of semantic data models. The approach contains expressive abstractions to model static and derived data, semantics, and behavior. The abstractions keep the data model closer to the problem domain, and can be translated into a relational (or other) implementation. The paper makes six contributions. First, a comprehensive set of data structuring abstractions are described. Second, the abstractions are compared to the entity-relationship and relational models. Third, semantic information inherent in the functional representation of the abstractions is identified. Fourth, a set of behavioral abstractions are described. Fifth, an algorithm that describes the dynamics between mathematically derived attributes of cooperating objects is presented. Sixth, weaknesses of object-oriented programming languages are identified