scholarly journals STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

2017 ◽  
Author(s):  
Yang He ◽  
Wei-Chen Chiu ◽  
Margret Keuper ◽  
Mario Fritz
Keyword(s):  
Semantic Segmentation ◽  
Data Driven ◽  
Temporal Data ◽  
Spatio Temporal

Event-Based Concepts for User-Driven Visualization

2009 ◽  
Vol 10 (1) ◽  
pp. 65-81 ◽  
Author(s):  
Christian Tominski
Keyword(s):  
General Model ◽  
Visual Representations ◽  
Data Driven ◽  
Relational Data ◽  
Temporal Data ◽  
Specific Data ◽  
Spatio Temporal ◽  
Visualization Technology ◽  
Event Based

Visualization has become an increasingly important tool to support exploration and analysis of the large volumes of data we are facing today. However, interests and needs of users are still not being considered sufficiently. The goal of this work is to shift the user into the focus. To that end, we apply the concept of event-based visualization that combines event-based methodology and visualization technology. Previous approaches that make use of events are mostly specific to a particular application case, and hence, can not be applied otherwise. We introduce a novel general model of event-based visualization that comprises three fundamental stages. (1) Users are enabled to specify what their interests are. (2) During visualization, matches of these interests are sought in the data. (3) It is then possible to automatically adjust visual representations according to the detected matches. This way, it is possible to generate visual representations that better reflect what users need for their task at hand. The model's generality allows its application in many visualization contexts. We substantiate the general model with specific data-driven events that focus on relational data so prevalent in today's visualization scenarios. We show how the developed methods and concepts can be implemented in an interactive event-based visualization framework, which includes event-enhanced visualizations for temporal and spatio-temporal data.

scholarly journals Introduction to spatio-temporal data driven urban computing

2020 ◽  
Vol 38 (3) ◽  
pp. 561-562
Author(s):  
Shuo Shang ◽  
Kai Zheng ◽  
Panos Kalnis
Keyword(s):  
Data Driven ◽  
Temporal Data ◽  
Urban Computing ◽  
Spatio Temporal

scholarly journals Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongqu Lv ◽  
Wensi Cheng
Keyword(s):  
Data Mining ◽  
Technical Efficiency ◽  
Spatial Data ◽  
Stochastic Frontier ◽  
Data Driven ◽  
Stochastic Frontier Model ◽  
Temporal Data ◽  
Spatial Weight Matrix ◽  
Spatial Weight ◽  
Spatio Temporal

Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time lag effect. If the traditional spatial statistical method is used, the setting method of spatial weight matrix is often questioned. To solve this series of problems, one possible idea is to design a spatial data mining process based on stochastic frontier analysis. Firstly, the stochastic frontier model should be improved to analyze spatio-temporal data. In order to accurately measure the technical efficiency in the case of dual correlation between time and space, a more effective spatio-temporal stochastic frontier model method is proposed. Meanwhile, based on the idea of generalized moment estimation, an estimation method of spatiotemporal stochastic frontier model is designed, and the consistency of estimators is proved. In order to ensure that the most appropriate spatial weight matrix can be selected in the process of model construction, the K -fold crossvalidation method is adopted to evaluate the prediction effect under the data-driven idea. This set of spatio-temporal data mining methods will be used to measure the technical efficiency of high-tech industries in various provinces of China.

Spatial-temporal data collection process models using mobile sinks

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova
Keyword(s):  
Energy Consumption ◽  
Operation Time ◽  
Point Of View ◽  
Process Models ◽  
Temporal Data ◽  
Network Nodes ◽  
Mobile Sinks ◽  
Autonomous Operation ◽  
Data Collection Process ◽  
Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

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