Journalism in the Age of Data  [stanford.edu] is a must-see documentary of about 54 minutes, ‘starring’ some of the most talented and well-known data visualization practitioners of today. The covered topics are wide, but include contemporary issues like Visualization in Journalism, Telling “Data Stories”, Exploring Data or Technology and Tools. It was produced during the [...]

Today’s digital libraries and data centers store huge amounts of data, collected, for example, by scientific experimentation, earth observation, or simulation. The common approach to support user navigation and retrieval in these libraries operates on meta-information that is manually appended to the datasets.

Especially for large datasets, this approach quickly reaches its limits due to scalability with respect to manual annotation.

Exploring video data by simply watching does not scale for large databases.This problem arises in a wide variety of domains, such as scientificvideo analysis, digital life streams, and video surveillance.

Motivated by a mini challenge of the contest of the IEEE Symposium on Visual Analytics Science and Technology 2009 (Detecting the encounter of persons in a provided video stream utilizing the techniques of visual analytics), we propose an approach for fast identification of relevant objects based on the properties of their trajectories.

Of the machine components that are part of visual analytics, human-computer interaction, and visualization, the display has one of the largest effects.

The display determines the physical size of the viewport, the range of effective field-of-view and resolution that is possible, and the types of feasible interaction modalities. All other interactions must be placed in the context of the display. If you have ever seen a movie on a large screen TV, then you have been surely impressed with the captivating display that makes the viewing innately attractive.

Volume graphics deals with the analysis, synthesis and presentation of volumetric phenomena, both static and time-varying. Specifically, it includes topics, both volumetric and point-based, related to the acquisition, reconstruction and transformation of volume data as well as feature analysis, information extraction and rendering. Research papers are solicited that present original, unpublished results concerning all aspects of volume graphics and point-based graphics. We especially invite research contributions that report computational techniques.

The aim of this work is to survey and reflect on the various ways visualization and data mining can be integrated to achieve effective knowledge discovery by involving the best of human and machine capabilities. Following a bottom-up bibliographic research approach, the article categorizes the observed techniques in classes, highlighting current trends, gaps, and potential future directions for research.

Daniel A. Keim and Florian Mansmann and Daniela Oelke and Hartmut Ziegler, Visual Analytics: Combining Automated Discovery with Interactive Visualizations, 2008, Discovery Science,pp. 2-14, ISBN 978-3-540-88410-1., [download], [bibtex]

@inproceedings{Keim-5,
  author = {Daniel A. Keim and Florian Mansmann and Daniela Oelke and Hartmut Ziegler},
  title = {Visual Analytics: Combining Automated Discovery with Interactive Visualizations},
  month = {December},
  year = {2008},
  note = {Discovery Science,pp. 2-14, ISBN 978-3-540-88410-1.},
}

The term Visual Analytics has been around for almost five years by now, but still there are on-going discussions about what it actually is and in particular what is new about it. The core of our view on Visual Analytics is the new enabling and accessible analytic reasoning interactions supported by the combination of automated [...]