Visualization for data-intensive science

ince the advent of computing, the world has experienced an information " big bang " : an explosion of data. The amount of information being created is increasing at an exponential rate. Since 2003, digital information has accounted for 90 percent of all information produced [1], vastly exceeding the amount of information on paper and on film. One of the greatest scientific and engineering challenges of the 21st century will be to understand and make effective use of this growing body of information. Visual data analysis, facilitated by interactive interfaces, enables the detection and validation of expected results while also enabling unexpected discoveries in science. It allows for the validation of new theoretical models, provides comparison between models and datasets, enables quantitative and qualitative querying, improves interpretation of data, and facilitates decision making. Scientists can use visual data analysis systems to explore " what if " scenarios, define hypotheses, and examine data using multiple perspectives and assumptions. They can identify connections among large numbers of attributes and quantitatively assess the reliability of hypotheses. In essence, visual data analysis is an integral part of scientific discovery and is far from a solved problem. Many avenues for future research remain open. In this article, we describe visual data analysis topics that will receive attention in the next decade [2, 3].