TY - GEN
T1 - A visual decision-guided tool
T2 - 2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014
AU - Ngan, Chun Kit
PY - 2014
Y1 - 2014
N2 - We propose a Visual Decision-Guided Tool that integrates optimization programming into geo-data visualization to determine the best path for rescue and recovery missions. First, we will develop the Top-k Objected-oriented Smoothest Paths model which captures the object dynamics of geospatial temporal network in a terrain over a time horizon. These objects include stationary entities, mobile objects, and route segments. Second, we will extend the Smoothest Path Algorithm (SPA) to be a dynamic learning algorithm, i.e., the Time-varying Smoothest Path Algorithm, which integrates the object dynamics to learn the top-k smoothest routes at each instance of time. The main advantage offered by the SPA extension is its lower logarithmic time complexity, i.e., O(NlogN), where N is the number of nodes in a terrain. Finally, we will develop a new design of visual displays that enable military operators to analyze other crucial factors, such as vehicle types, weather severity, and soldiers' specialty levels, which are required to be interpreted by human perception, cognition, and knowledge to select the best path among the top-k smoothest routes at each instance of time for rescue and recovery missions.
AB - We propose a Visual Decision-Guided Tool that integrates optimization programming into geo-data visualization to determine the best path for rescue and recovery missions. First, we will develop the Top-k Objected-oriented Smoothest Paths model which captures the object dynamics of geospatial temporal network in a terrain over a time horizon. These objects include stationary entities, mobile objects, and route segments. Second, we will extend the Smoothest Path Algorithm (SPA) to be a dynamic learning algorithm, i.e., the Time-varying Smoothest Path Algorithm, which integrates the object dynamics to learn the top-k smoothest routes at each instance of time. The main advantage offered by the SPA extension is its lower logarithmic time complexity, i.e., O(NlogN), where N is the number of nodes in a terrain. Finally, we will develop a new design of visual displays that enable military operators to analyze other crucial factors, such as vehicle types, weather severity, and soldiers' specialty levels, which are required to be interpreted by human perception, cognition, and knowledge to select the best path among the top-k smoothest routes at each instance of time for rescue and recovery missions.
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U2 - 10.1109/CSCI.2014.124
DO - 10.1109/CSCI.2014.124
M3 - Conference contribution
AN - SCOPUS:84902684864
SN - 9781479930098
T3 - Proceedings - 2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014
SP - 226
EP - 228
BT - Proceedings - 2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014
PB - IEEE Computer Society
Y2 - 10 March 2014 through 13 March 2014
ER -