PIVE: A Per-Iteration Visualization Environment for Supporting Real-time Interactions with Computational Methods

Abstract

Visual analytics has been gaining increasing interest due to its fascinating characteristic that leverages both humans’ visual perception and the power of computing. Although various computational methods are being proposed, they do not properly support visual analytics. One of the biggest obstacles towards their real-time visual analytic integration is their high computational complexity. As a way to tackle this problem, this paper presents PIVE, a Per-Iteration Visualization Environment for supporting real-time interactive visualization with computational methods. The main idea behind PIVE is that most advanced computational methods work by refining the solution iteratively. By visually delivering the result from each iteration to users, the proposed framework enables users to quickly acquire the information that the computational method provides as well as the ability to perform continuous interactions with them in real time. We show the effectiveness of PIVE in terms of real-time visualization and interaction capabilities by customizing various dimension reduction methods such as principal component analysis, multidimensional scaling, and t-distributed stochastic neighborhood embedding, and clustering method s such as k-means and latent Dirichlet allocation.