Fuzzy Spatiotemporal Data Research Articles

F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases

In the literature, several studies have focused on introducing fuzzy extensions to the relational and/or object database models in order to store the imprecision. Indeed, on one hand, fuzzy EER and fuzzy UML are both applied for fuzzy object-oriented database modelling. On the other hand, Fuzzy ER is adapted for fuzzy relational database models. All these previous fuzzy conceptual modelling methods are not adapted to fuzzy spatiotemporal data. In this paper, we propose an approach for modelling imprecise data in object and relational databases based on the representation of data using connected and normalised fuzzy sets stored via α-cuts. The approach is applied to geographical information systems in order to handle imprecise spatiotemporal data.

Indexing Fuzzy Spatiotemporal Data for Efficient Querying: A Meteorological Application

Spatiotemporal data, in particular fuzzy and complex spatial objects representing geographic entities and relations, is a topic of great importance in geographic information systems and environmental data management systems. For database researchers, modeling and designing a database of fuzzy spatiotemporal data and querying such a database efficiently have been challenging issues due to complex spatial features and uncertainty involved. This paper presents an integrated approach to modeling, indexing, and efficiently querying spatiotemporal data related to fuzzy spatial and complex objects and spatial relations. As our case study, we design and implement a meteorological database application that involves fuzzy spatial and complex objects, and a spatiotemporal index structure, and supports various types of spatial queries including fuzzy spatiotemporal queries. Our implementation is based on an intelligent database system architecture that combines a fuzzy object-oriented database with a fuzzy knowledge base.

Incorporating fuzziness in spatiotemporal XML and transforming fuzzy spatiotemporal data from XML to relational databases

Since XML could benefit greatly from database support and more specifically from relational database systems, we study the methodology of modeling fuzzy spatiotemporal data in XML and transforming fuzzy spatiotemporal data from XML to relational databases as well. To accomplish this, we devise a fuzzy spatiotemporal data model in XML to capture the semantics of fuzzy spatiotemporal features. To allow for better and platform independent sharing of fuzzy spatiotemporal data stored in a relational format, we propose a temporal edge approach to transform fuzzy spatiotemporal XML data into relational databases. The unique feature of our approach is that no schema information is required for transformation of fuzzy spatiotemporal data. Moreover, temporal, spatial, and fuzzy features of fuzzy spatiotemporal data in XML documents are taken into consideration. Finally, the experimental results demonstrate the performance advantages of our approach. Such approach of transformation could provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from XML to relational databases.

Fuzzy Spatiotemporal Data Modeling and Operations in XML

Because increasing requirements of fuzzy spatiotemporal applications are attracting much attention from both academia and industry, it is challenging to model fuzzy spatiotemporal data and effectively operate them. However, various researches are studied in traditional databases that impose strict restrictions, and relatively little work has been carried out in modeling and operating fuzzy spatiotemporal data in XML. In this paper, we propose a novel approach to model fuzzy spatiotemporal data based on XML. On the basis of the model, we investigate how to represent fuzzy spatiotemporal data in XML documents and extend the XML schema so that it is possible to describe fuzzy spatiotemporal data and capture the structural information of fuzzy spatiotemporal XML documents. Furthermore, we give algorithms for fuzzy operations, containing node operations and topological relationship operations. Finally, we apply our model in meteorological events.

Querying fuzzy spatiotemporal data using XQuery

How to accurately handle querying criteria based on user's preferences has become a key technology for querying fuzzy spatiotemporal data, typically collected by modern monitoring devices such as GPS receivers and sensors. However, although fuzzy log

Determining topological relationship of fuzzy spatiotemporal data integrated with XML twig pattern

How to determine topological relationships is one of the most important operations on fuzzy spatiotemporal data. The proposed strategies impose strict restrictions on structure and data types of fuzzy spatiotemporal data, and fall short in their abilities to handle fuzzy attributes extension and fuzzy time extension. To overcome these limitations, in this paper, we first establish a fuzzy spatiotemporal data model based on XML. Then, we propose strategies of transforming two general fuzzy spatiotemporal data trees into one binary fuzzy spatiotemporal data tree. In succession, an effective algorithm to match the desired twigs is proposed after extending the region coding scheme to compatible with fuzzy spatiotemporal data. Our approach adopts XML twig pattern technique to determine topological relationship continuously so that it can reduce unnecessary execution time of querying the desired nodes. More importantly, we use a pointer array to eliminate unnecessary execution time of twig matching. Finally, the experimental results demonstrate the performance advantages of our approach.