Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach-Reference-Cited by-同舟云学术

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Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

Published:2016-06-07 Issue:1 Volume:31 Page:168-189
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Li Jing¹,Zhang Tong²³,Liu Qing¹,Yu Manzhu⁴

Affiliation:

1. Department of Geography and the Environment, University of Denver, Denver, CO, USA

2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

3. Collaborative Innovation Center of Geospatial Technology, Wuhan, China

4. Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA

Funder

Natural Science Foundation of China

2014 Kunshan High-level Innovative and Entrepreneurial Talents Program under grant

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2016.1194424

Reference27 articles.

1. Web based visualization of large climate data sets

2. Local Indicators of Spatial Association-LISA

3. Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing

4. A Combined Fuzzy-Neural Network Model for Non-Linear Prediction of 3-D Rendering Workload in Grid Computing

5. Job Scheduling Strategies for Parallel Processing

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatiotemporal data mining: a survey on challenges and open problems;Artificial Intelligence Review;2021-04-15

2. Automatic cloud resource management for interactive remote geovisualization;Transactions in GIS;2018-11-04

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