A data model for regions with indeterminate boundaries

Most of spatial phenomena like natural vegetation units and land use areas constantly change over time and have uncertainty spatial extents. Till now, a considerable number of data models have been proposed for spatial objects with sharp boundaries as well as with indeterminate boundaries. However, they mainly concern space and time or space and fuzziness and not yet integrate them into a single unified framework. This paper introduces a formal definition of the conceptual fuzzy spatiotemporal data model, called FSTDM for fuzzy regions based on fuzzy set theory. We also contribute a method of manipulating queries with the presence of both temporal predicate and fuzzy spatial predicate in the condition clause efficiently. We then implement a prototype system.Through the experimental results, we prove that our work can be used to build a specialized system such as GIS, spatial database, and so on.     

Development operators on 4D moving object databases

In this paper we propose four-dimensional (4D) operators,which can be used to deal with sequential changes of topological relationships between 4D moving objects and we call them 4D development operators. In contrast to the existing operators,we can apply the operators to real applications on 4D moving objects. We also propose a new approach to define them. The approach is based on a dimension-separated method,which considers x-y coordinates and ( coordinates separately. In order to show the applicability of our operators,we show the algorithms for the proposed operators and development graph between 4D moving objects.     

A data model for regions with indeterminate boundaries

Most of spatial phenomena like natural vegetation units and land use areas constantly change over time and have uncertainty spatial extents. Till now,a considerable number of data models have been proposed for spatial objects with sharp boundaries as well as with indeterminate boundaries. However,they mainly concern space and time or space and fuzziness and not yet integrate them into a single unified framework. This paper introduces a formal definition of the conceptual fuzzy spatiotemporal data model,called FSTDM for fuzzy regions based on fuzzy set theory. We also contribute a method of manipulating queries with the presence of both temporal predicate and fuzzy spatial predicate in the condition clause efficiently. We then implement a prototype system. Through the experimental results,we prove that our work can be used to build a specialized system such as GIS,spatial database,and so on.     

Development operators on 4D moving object databases

In this paper we propose four-dimensional (4D) operators, which can be used to deal with sequential changes of topological relationships between 4D moving objects and we call them 4D development operators. In contrast to the existing operators, we can apply the operators to real applications on 4D moving objects. We also propose a new approach to define them. The approach is based on a dimension-separated method, which considers x-y coordinates and z coordinates separately. In order to show the applicability of our operators, we show the algorithms for the proposed operators and development graph between 4D moving objects.     

Selecivity estimation using compressed spatial information

Spatial selectivity estimation is one of the essential studies to get query responses rapidly and accurately with the limitation of memory space. Currently, there exist several spatial selectivity estimation techniques such as random sampling, histogram, and parametric. Especially，Cumulative Density Histogram guarantees accurate estimation for rectangle object which has multiple-count problem. However，it requires large memory space because of retaining four sub-histograms for spatial data. Therefore in this paper， we propose a new technique Cumulative Density Wavelet Histogram，called CDWH，which is the combination of Cumulative Density Histogram and Haar Wavelet Transform，a compressed technique. The proposed method simultaneously takes full advantage of their strong points，high accuracy provided by the former and economization of memory space supported by the latter. Consequently，our technique is able to support estimates with relatively low error and retain similar estimates even if memory space is small.     

Selectivity estimation using compressed spatial information

Spatial selectivity estimation is one of the essential studies to get query responses rapidly and accurately with the limitation of memory space. Currently, there exist several spatial selectivity estimation techniques such as random sampling, histogram, and parametric. Especially, Cumulative Density Histogram guarantees accurate estimation for rectangle object which has multiple-count problem. However, it requires large memory space because of retaining four sub-histograms for spatial data. Therefore in this paper,we propose a new technique Cumulative Density Wavelet Histogram, called CDWH, which is the combination of Cumulative Density Histogram and Haar Wavelet Transform, a compressed technique. The proposed method simultaneously takes full advantage of their strong points, high accuracy provided by the former and economization of memory space supported by the latter. Consequently, our technique is able to support estimates with relatively low error and retain similar estimates even if memory space is small.