Fuzzy Object-oriented Data Model Research Articles

Representation and Reasoning of Fuzzy Object-Oriented Data Model with Description Logic

通过分析描述逻辑与面向对象数据模型之间的关系,研究了基于描述逻辑的模糊面向对象数据(fuzzy object-oriented data,简称FOOD)模型的表示与推理.首先,进一步研究了FOOD 模型,提出了FOOD 模型的形式化定义和语义定义,以便更好地在FOOD模型与模糊描述逻辑之间建立对应关系;然后,针对FOOD模型的特点和推理需求,介绍了模糊描述逻辑f-ALCIQ.在此基础上,研究了基于f-ALCIQ 的FOOD 模型的表示与推理,包括:实现了从 FOOD 模型到f-ALCIQ 知识库在结构层和实例层上的转化,即实现了从FOOD 模型到f-ALCIQ TBox 的转化,以及从FOOD 模型相应的数据库实例到f-ALCIQ ABox 的转化;进而,基于转化得到的f-ALCIQ 知识库,研究了如何利用 f-ALCIQ 的推理机制对FOOD 模型的推理问题(一致性、包含性和冗余性等)进行推理;最后,设计并实现了基于f-ALCIQ 的模糊描述逻辑推理机(FRsQ 推理机),实现了对FOOD 模型和f-ALCIQ 模糊概念知识的自动推理.

FOOD Index: A Multidimensional Index Structure for Similarity-Based Fuzzy Object Oriented Database Models

A fuzzy object-oriented data model is a fuzzy logic-based extension to an object-oriented database model that permits uncertain data to be explicitly represented. The fuzzy object-oriented database (FOOD) model is one of the proposed models in the literature to handle uncertainty in object-oriented databases. Several kinds of fuzziness are dealt with in the FOOD model, including fuzziness at attribute level and between object and class and between class and superclass relations. The traditional index structures do not allow efficient access to both crisp and fuzzy objects for fuzzy object-oriented databases since they are not efficient enough in processing both crisp and fuzzy queries. In this study, we propose a new index structure, namely a FOOD index (FI), to deal with different kinds of fuzziness in fuzzy object-oriented databases and to support multidimensional indexing. In this paper, we describe this proposed index structure and show how it supports various types of flexible queries, and evaluate its performance for exact, range, and fuzzy queries.

A hybrid object based model combining probability and fuzzy set theories

Although there have been many fuzzy object-oriented data model proposed, and a bit less for probabilistic ones, models combining the relevance and strength of both fuzzy set theory and probability theory appear to be sporadic. This paper introduces our extension of Eiter et al.'s probabilistic object base model with two key features: uncertain and imprecise attribute values are represented as probability distributions on a set of fuzzy set values; class methods with uncertain and imprecise input and output arguments are formally integrated into the new model. A probabilistic interpretation of relations on fuzzy set values is proposed for their combination with probability degrees. Then the syntax and semantics of fuzzy-probabilistic object base schemas, instances, and selection operation are defined.

A fuzzy object-oriented data model for managing vague and uncertain information

In this paper, a fuzzy Object-Oriented Data model (FOOD) is defined based on the extension of a Graph-based Object model (D. Lucarella and A. Zanzi A graph-oriented data model, in Database and Expert Systems Applications, R. Wagner and H. Toma, Eds., Springer-Verlag, Berlin, 1996, pp. 197-206), in order to manage both crisp and imperfect information. These capabilities are requisites of many current applications dealing with data of different nature and with complex interrelationships. The model is based on a visual paradigm which supports both the representation of the data semantics and the direct browsing of the information. In the extended model both the database scheme and instances are represented as directed labeled graphs in which the fuzzy and uncertain information has its own representation.

Design and implementation issues in the fuzzy object-oriented data model

Uncertainty modeling and data manipulation are desirable in object-oriented database systems to handle complex objects with imprecise properties. In this paper, we present an enhancement to the fuzzy object-oriented data model (FOOD) (George et al., Fuzzy Sets and Systems 60 (3) (1993) 259–272), that permits a truer representation of different types of uncertainty. Uncertainty is extended to the class. efinition level and is the basis for the determination of the membership of an object in a class. We describe a software architecture for the implementation of the model and discuss the significant details of a prototype implemented using the EXODUS storage manager (ESM).