Section: Overall Objectives

Objectives

All human activities are being transformed by our rapidly increasing abilities to collect, manage and understand vast amounts of data. A 2003 study estimated that the amount of data produced in the world was increasing by 50% each year (Peter Lyman and Hal R. Varian. How much information. Retrieved from http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/ , 2003.). According to SearchEngineWatch (http://www.searchenginewatch.com ), the amount of information made available through Internet search engines has grown exponentially for the last decade, and major Web search engines currently index more than 2 billion documents. However, since our brains and sensory capacities have not evolved in the meantime, gaining competitive advantage from all this data depends increasingly on the effectiveness with which we support human abilities to perceive, understand, and act on the data.

With this increase of data, the traditional scientific method of applying model-based analysis to understand the data is no longer sufficient. We have access to data that we have never encountered before and have little or no idea of applicable models. Therefore, we need to explore them first to gain insights and eventually find models. This process has already been promoted by John Tukey in his 1977 book on Exploratory Data Analysis (John W. Tukey. Exploratory Data Analysis. Addison-Wesley, 1977.) which has become a branch of the domain of statistics. Whereas EDA is ultimately interested in finding models, data exploration can also reveal relevant facts that are, in themselves interesting and important.

Aviz (Analysis and VIsualiZation) is a multidisciplinary project that seeks to improve visual exploration and analysis of large, complex datasets by tightly integrating analysis methods with interactive visualization. It focuses on five research themes:

• Methods to visualize and smoothly navigate through large datasets;

• Efficient analysis methods to reduce huge datasets to visualizable size;

• Visualization interaction using novel capabilities and modalities;

• Evaluation methods to assess the effectiveness of visualization and analysis methods and their usability;

• Engineering tools for building visual analytics systems that can access, search, visualize and analyze large datasets with smooth, interactive response.