Foreword to special issue on knowledge assisted visualization

Most of the existing visualization techniques and systems were not designed to utilize the knowledge and information derived from the process of scientific visualization or from abstract data analysis. As visual exploration is an inherently iterative process, it is desirable to enable more effective visualizations by utilizing information about the scientific data to be visualized (e.g., high level abstract characterization and findings) or information about the visualization process itself (e.g., users’ chosen visualization parameters and abstractions). The combination of such information from different visualization processes can also infer new knowledge that can aid data visualization in an intelligent manner if it is stored and organized in a structured fashion. Two workshops on knowledge assisted visualization (KAV) have been organized in 2007 and 2008 in conjunction with the IEEE Visualization Conference. The goal of the workshops was to stimulate the research efforts for knowledge enabled data visualization. This special issue contains revised and expanded versions of the four best papers selected from these workshops. Three papers give concrete examples of how knowledge assisted visualization can be used in practice, while the fourth discusses a general framework for such tasks. The paper, ‘‘Knowledge Assisted Visualization of Seismic Data’’ by Patel et al. describes novel techniques for knowledge assisted annotation and computer-assisted interpretation of seismic data for oil and gas exploration. Existing procedures for oil and gas search are improved by introducing a computer-assisted approach enabling a quicker, more focused and accurate interpretation. The proposed approach also enables representations of hypotheses and knowledge to annotate the data. The second paper, ‘‘Steady Visualization of the Dynamics in Fluids Using Epsilon-Machines’’ by Jaenicke et al., proposes to use epsilonmachines for the visualization of the dynamics in fluids. Epsilonmachines can be thought of as finite state machines that are visualized as directed graphs. Given a local past of a field position, the nodes of the graph consists of all the information needed to predict the future of this position. Edges in the graph indicate transition probabilities in successive time-steps. Hence, the visualization of the epsilon-machine graph provides a concise and highly compressed steady visualization of the system’s dynamics. The paper describes the construction and visualization of epsilon-machines and how interaction mechanisms with the physical domain allow for a detailed analysis of data sets describing fluid dynamics. The third paper, ‘‘A High-Dimensional Feature Clustering Approach to Support Knowledge-Assisted Visualization’’ by EunJu Nam et al., proposes a framework on which visualization expertise is stored directly with the visualization method in a data set. This expertise can then be utilized for user guidance in the data