Large Multiples : exploring the large-scale scattergun approach to visualization and analysis
We create 2.5 quintillion bytes of data every day. A whole 90% of the world’s data was created in the last two years.1 One contribution to this massive bulk of data is Twitter: Twitter users create 500 million tweets a day,2 which fact has greatly impacted social science  and journalism . Network analysis is important in social science , but with so much data there is a real danger of information overload, and there is a general need for tools that help users navigate and make sense of this. Data exploration is one way of analyzing a data set. Exploration-based analysis is to let the data suggest hypotheses, as opposed to starting out with a hypothesis to either confirm or refute. Visualization is an important exploration tool. Given the ready availability of large-scale displays , we believe that an ideal visual exploration system would leverage these, and leverage the fact that there are many different ways to visualize something. We propose to use wall- sized displays to provide many different views of the same data set and as such let the user explore the data by exploring visualizations. Our thesis is that a display wall architecture [1, 42] is an ideal platform for such a scheme, providing both the resolution and the compute power required. Proper utilization of this would allow for useful sensemaking and storytelling. To evaluate our thesis we have built a system for gathering and analyzing Twitter data, and exploring it through multiple visualizations. Our evaluation of the prototype has provided us with insights that will allow us to create a practicable system, and demonstrations of the prototype has uncovered interesting stories in our case study data set. We find that it is strictly necessary to use clever pre-computation, or pipelining, or streaming to meet the strict latency requirements of providing visualization interactively fast. Our further experiments with the system have led to new discoveries in streaming graph processing.
PublisherUiT Norges arktiske universitet
UiT The Arctic University of Norway
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