Contourline based modelling of vague regions

This paper introduced a model based on contour lines to represent a vague region modeled as a fuzzy set. The model allows the user to adjust the accuracy of the membership function to the needs of the application and can enforce the continuity of this function whenever desirable

Adapting TIN layers to represent fuzzy geographic information

One of the latest research topics in geographic information systems (GIS for short) is the modeling of fuzzy (imprecise or vague) and/or uncertain information. A GIS usually contains large amounts of data and requires very specific operations to manage (geographic) data, both of which are described in a geographic database model. In this paper, the basic concepts for a data model describing the structure of and operations on fuzzy or uncertain geographic information are presented. Specific for the presented model is the adaptation of triangulated irregular networks (TINs), commonly used in GIS (e.g. to model altitudes), for the modelling of fuzzy (geographic) data

Fuzzy and uncertain spatio-temporal database models : a constraint-based approach

In this paper a constraint-based generalised object-oriented database model is adapted to manage spatiotemporal information. This adaptation is based on the definition of a new data type, which is suited to handle both temporal and spatial information. Generalised constraints are used to describe spatio-temporal data, to enforce integrity rules on databases, to specify the formal semantics of a database scheme and to impose selection criteria for information retrieval

Problems and opportunities of applying data-& audio-mining techniques to ethnic music

[TODO] Add abstract here

Numerical properties of fuzzy regions: Surface area

This paper concerns the modelling of fuzzy information in geographic databases. In the past, a theoretical model for fuzzy regions has been presented, along with various operations useful in a geographic database: union, intersection, topology, bounding rectangle, etc. and feasible models for implementation based on this theoretical model. Now, the attention is directed at some of the problems that can occur when determining numerical properties of fuzzy regions: what type of result is expected (and desired), and how does this impact the definitions of the operations. As an example, the definition of the surface area of a fuzzy set is studied in more detail

Fuzzy regions: Theory and applications

Traditionally, information in geographic information systems (GIS) is represented as crisp information. While for many applications, this is a good enough approximation of reality, some models would benefit from having the inherent imprecision or uncertainty incorporated in the model. In literature, several ideas and concepts to improve on the crisp models have been considered. In the past, we have presented different models to represent and to work with the concept of regions, defined using fuzzy set theory in GIS systems. For such fuzzy regions, a number of approaches already have been described in detail. In this paper, we will elaborate on a fuzzy set approach and practical implementations of the concept. Apart from the concept, two developed techniques (one based on triangulated networks, one based on bitmap models) are presented along with some of the operators. An overview of application fields is provided to illustrate where and how the techniques can be used