Visual exploration of data enables users and analysts observe interesting patterns that can trigger new research for further investigation. With the increasing availability of Linked Data, facilitating support for making sense of the data via visual exploration tools for hypothesis generation is critical. Time and space play important roles in this because of their ability to illustrate dynamicity, from a spatial context. Yet, Linked Data visualization approaches typically have not made efficient use of time and space together, apart from typical rather static multivisualization approaches and mashups. We developed ELBAR explorer that visualizes a vast amount of scientific observational data about the Brazilian Amazon Rainforest. The core contribution is a novel mechanism for animating between the different observed values, thus illustrating the observed changes themselves.
ELBAR-explorer will be demoed at ISWC2014 in October, 2014. The following paper will give more details:
- Suvodeep Mazumdar and Tomi Kauppinen. Visualizing and Animating Large-scale Spatiotemporal Data with ELBAR Explorer. Satellite Events Poceedings of the 13th International Semantic Web Conference (ISWC2014), Riva del Garda, Trentino, Italy, October, 2014 (in press). [BibTeX]