Relative space-based GIS data model to analyze the group dynamics of moving objects

3D data model is an indispensable component to any 3D GIS, and forms the basis of 3D spatial analysis and representation. At present, plenty of representative 3D data models are proposed. However, existing models neglect the display result and the consumption of storage space. Based on the analysis of existing 3D GIS data model, a 3D surface model is proposed for fast visualization in this paper, which is composed of node, segment and triangle. The data structure and formal representation of the proposed 3D surface model is developed to organize and store data of 3D model. Finally, an experiment is made to compare this 3D surface model with other 3D data model, and the result demonstrates that the 3D surface model proposed in this paper is superior to the existing data model in terms of data volume, moreover, it can acquire fast visualization speed.

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Implementation of object-oriented GIS data model with topological relations between spatial objects

10.1117/12.2011995 ◽

2013 ◽

Cited By ~ 1

Author(s):

Youliang Chen ◽

Zhaoru Wang ◽

Zhicheng Chen

Keyword(s):

Data Model ◽

Object Oriented ◽

Topological Relations ◽

Spatial Objects ◽

Gis Data

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A location prediction method for work-in-process based on frequent trajectory patterns

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405417708222 ◽

2017 ◽

Vol 233 (1) ◽

pp. 306-320 ◽

Cited By ~ 1

Author(s):

Haoshu Cai ◽

Yu Guo ◽

Kun Lu

Keyword(s):

Data Model ◽

Data Analytics ◽

Moving Objects ◽

Prediction Method ◽

Shop Floor ◽

Location Prediction ◽

Trajectory Data ◽

Logical Space ◽

Work In Process ◽

Series Of Experiments

In the data-rich manufacturing environment, the production process of work-in-process is described and presented by trajectories with manufacturing significance. However, advanced approaches for work-in-process trajectory data analytics and prediction are comparatively inadequate. However, the location prediction of moving objects has drawn great attention in the manufacturing field. Yet most approaches for predicting future locations of objects are originally applied in geography domain. When applied to manufacturing shop floor, the prediction results lack manufacturing significance. This article focuses on predicting the next locations of work-in-process in the workshop. First, a data model is introduced to map the geographic trajectories into the logical space, in order to convert the manufacturing information into logical features. Based on the data model, a prediction method is proposed to predict the next locations using frequent trajectory patterns. A series of experiments are performed to examine the prediction method. The experiment results illustrate the impacts of the user-defined factors and prove that the proposed method is effective and efficient.

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