Fuzzy Spatiotemporal Data Modeling Based on UML

Abstract

With the wide application of spatiotemporal data, more and more data need to be modeled. Fuzziness is one of the important characteristics of spatiotemporal data, but most of the existing spatiotemporal data models are regarded as accurate data and most of the spatiotemporal data models are static. The purpose of this paper is to build a fuzzy spatiotemporal data model based on UML by expanding the standard modeling language UML. On this basis, the historical topological state, effective time, and transaction time are added to make the model dynamic, that is, the states of multiple times are combined together to form a timeline to represent the development process of a fuzzy spatiotemporal entity. Considering the fuzziness of data and the dynamic development of objects, the model can describe the fuzzy spatiotemporal objects better, and make the model dynamic. At the same time, the operation of the UML class diagram is added to make the model data exercisable and compared according to their own needs, which increases the practicability of the model. Finally, an example of land desertification in Alashan is given, to prove the practicability of the model constructed in this paper. INDEX TERMS Dynamic, historical topological state, modeling, spatiotemporal, UML.

Intuitionistic Fuzzy Sets for Spatial and Temporal Data Intervals

“…Often, spatial and temporal data are combined in fuzzy spatiotemporal models. These arise from integrating multisource data [17] and from data modeled by UML [18]. Additionally, rough set techniques have been used in the development of temporal information systems [19,20].…”

“…So, by pruning D 9 the aggregation result is S': N1 has only one entry and thus its value is just D 12 . Proceeding next to aggregate P1 and P2 and discussing the results where the interval value for averaging is followed by the merging, for P1, P1: {SP1 Avg = [11, 17.6]; SP1 Mg = [9,18];…”

Modeling Objects with Uncertain Behaviors.

“…The OMG is also working on a metamodel for the precise specification of uncertainty (PSUM) (Object Management Group 2017), based on the U-Model (Zhang et al 2016) and the Uncertum conceptual model (Zhang et al 2019). Some works address particular types of uncertainties using different notations and logics, namely, Measurement uncertainty (Burgueño, Mayerhofer, et al 2019;Bertoa et al 2020); Design uncertainty (Zhang et al 2018;Famelis et al 2012;Salay et al 2013); Occurrence uncertainty (Burgueño et al 2018); Belief uncertainty (Burgueño, Clarisó, et al 2019;Martín-Rodilla & Gonzalez-Perez 2019), or Data uncertainty (Jing et al 2008;Zhou et al 2009;Wang & Bai 2019). However, the specification of the behavior of agents subject to uncertainty, which combines several of these types of uncertainty, has received less attention by the modeling community (Giese et al 2014).…”

Efficient land desertification detection using a deep learning‐driven generative adversarial network approach: A case study

“…For instance, in Reference 1, an approach using spectral mixture analysis (SMA) and a decision‐tree algorithm has been proposed for analyzing the grassland desertification in Ningxia, China using Landsat time‐series images. In Reference 8, a method based on fuzzy spatiotemporal data with UML modeling language is applied for desertification detection over a time interval of 40 years (1970–2010). The data are viewed as a timeline, where historical and topological states were incorporated in the model to make it dynamic considering the development process of the desertification data.…”