Conceptual Modeling Through Fuzzy Logic for Spatial Database (original) (raw)

Fuzzy object-oriented database modeling coupled with fuzzy logic

Fuzzy Sets and Systems, 1997

This paper presents a modeling approach which couples fuzzy object-oriented database modeling with fuzzy logic. The modeling approach introduced here handles fuzziness at attribute, object/class and class/superclass levels in addition to fuzziness in class/class relationships and various associations among classes. We utilize logical rules to define some of the crisp/fuzzy relationships and associations which cannot be presented easily with object-oriented modeling features alone in the class hierarchies. We think that incorporation of object-oriented database modeling with logic along with usage of fuzzy set theory simplifies the design of complex and knowledge-intensive applications and handles uncertainty effectively, therefore resulting in a powerful modeling framework. © 1997 Elsevier Science B.V.

Conceptual Modeling in Fuzzy Object- Oriented Databases Using Unified Modeling Language

2014

In real applications, information is often imprecise and vague. Exact information has become an essential part of modern database applications for making next generation information systems more human friendly. Fuzzy techniques have been widely used to represent such vague information in various database models and theories. Because of the efficiency of object oriented databases in handling complex object, this database model is extensively used in representing and manipulating fuzzy data. However there is less research done in the area modeling of fuzzy object-oriented database. In this paper, a conceptual model has been proposed for fuzzy object-oriented databases using unified modeling language.

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

International Journal of Intelligent Systems, 1999

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. ᮊ

A Constraint Based Fuzzy Object Oriented Database Model

Modeling and Applications, 2006

The objective of this chapter is to define a fuzzy object-oriented formal database model that allows us to model and manipulate information in a (true to nature) natural way. Not all the elements (data) that occur in the real world are fully known or defined in a perfect way. Classical database models only allow the manipulation of accurately defined data in an adequate way. The presented model was built upon an object-oriented type system and an elaborated constraint system, which, respectively, support the definitions of types and constraints. Types and constraints are the basic building blocks of object schemes, which, in turn, are used for defining database schemes. Finally, the definition of the database model was obtained by providing adequate data definition operators and data manipulation operators. Novelties in the approach are the incorporation of generalized constraints and of extended possibilistic truth values, which allow for a better representation of data(base) semantics.

Providing support for multiple collection types in a fuzzy object oriented spatial data model

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397), 1999

Fuzzy set approaches are particularly suitable for issues of modeling uncertainty in spatial data. Previous work of the authors describes a framework to support uncertainty by using an object-oriented approach to modeling spatial data. The original research focused on how to incorporate spatial data into a fuzzy object model. This paper expands upon that by discussing the implications of incorporating all collection types described in the ODMG object database standard in this framework. In addition, we will look at how future collection types may be incorporated into the framework.

Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases, 1999

In many geographical applications there is a need to model spatial phenomena not simply by sharply bounded objects but rather through vague concepts due to indeterminate boundaries. Spatial database systems and geographical information systems are currently not able to deal with this kind of data. In order to support these applications, for an important kind of vagueness called fuzziness, we propose an abstract, conceptual model of so-called fuzzy spatial data types (i.e., a fuzzy spatial algebra) introducing fuzzy points, fuzzy lines, and fuzzy regions. This paper ? focuses on de ning their structure and semantics. The formal framework is based on fuzzy set theory and fuzzy topology.

Conceptual modeling of geographic information system applications

An important research trend in databases is to handle different types of uncertainty at both conceptual and logical levels for various non-traditional applications that may involve imprecision and uncertainty that have been difficult to integrate cohesively in simple database models. In this study we describe how to conceptually model complex and uncertain information at the conceptual level (by utilizing the ExIFO 2 model) for Geographic Information System (GIS) applications. We also give the mapping algorithm that transforms the conceptual schema of GIS applications into the logical database structures by utilizing the fuzzy object-oriented database (FOOD) model at the logical level. The types of uncertainty that we mainly focus in this paper are fuzzy, null, and incomplete information related to objects and their properties, classes, and relationships among objects and classes of GIS applications.