Abstract :
The volume of available data has been growing exponentially, increasing data problem´s complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven´t scaled well for big data. Computational methods can improve VA´s scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.
Keywords :
data analysis; data visualisation; interactive systems; VA scability; big data interactive visualization; computational method customization; data problem complexity; data problem obscurity; iteration-level interactive visualization; low-precision computation; visual analytics; Algorithm design and analysis; Clustering algorithms; Data visualization; Principal component analysis; Real-time systems; Visual analytics; Algorithm design and analysis; Clustering algorithms; Convergence; Data visualization; Principal component analysis; Real-time systems; Visual analytics; big data; clustering; computer graphics; dimension reduction; iteration-level visualization; large-scale data; low-precision computation; visual analytics;
