Structural Decomposition Trees: Semantic and Practical Implications

Abstract. The visualization of high-dimensional data is a challenging research topic. Existing approaches can usually be assigned to either relation or value visualizations. Merging approaches from both classes into a single integrated strategy, Structural Decomposition Trees (SDTs) represent a completely novel visualization approach for high-dimensional data. Although this method is new and promising, statements on how to use and apply the technique in the context of real-world applications are still missing. This paper discusses how SDTs can be interpreted and interacted with to gain insights about the data more effectively. First, it is discussed what properties about the data can be obtained by an interpretation of the initial projection. These statements are also valid for other projections based on principal components analysis, addressing a frequent problem when applying this technique. Further, a detailed and task-oriented interaction guideline shows how provided interaction methods can be utilized effectively for data exploration. The results obtained by an application of these guidelines in air quality research indicate that much insight can be gained even for large and complex data sets. This justifies and further motivates the usefulness and wide applicability of SDTs as a novel visualization approach for high-dimensional data

Creation and collaboration: engaging new audiences for information visualization

In recent years we have seen information visualization technology move from an advanced research topic to mainstream adoption in both commercial and personal use. This move is in part due to many businesses recognizing the need for more effective tools for extracting knowledge from the data warehouses they are gathering. Increased mainstream interest is also a result of more exposure to advanced interfaces in contemporary online media. The adoption of information visualization technologies by lay users – as opposed to the traditional information visualization audience of scientists and analysts – has important implications for visualization research, design and development. Since we cannot expect each of these lay users to design their own visualizations, we have to provide them tools that make it easy to create and deploy visualizations of their datasets