Author_Institution :
Great Valley Sch. of Grad. Prof. Studies, Pennsylvania State Univ., Malvern, PA, USA
Abstract :
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.
Keywords :
data visualisation; geographic information systems; military computing; object-oriented programming; terrain mapping; SPA; cognition; dynamic learning algorithm; geodata visualization; geospatial temporal network; human perception; integrating optimization programming; logarithmic time complexity; military operators; objected oriented smoothest paths model; recovery missions; rescue missions; smoothest path algorithm; viable shortest path; visual decision guided tool; Algorithm design and analysis; Data mining; Data visualization; Heuristic algorithms; Three-dimensional displays; Vehicle dynamics; Visualization; data analytic; data visualization; decision optimization; decision support; query language; shortest path problems;