Current datasets often grow so large and complex that automated methods appear to be the only way to gain knowledge from them. A new Web-based tool being developed at Carnegie Mellon University (CMU) offers the option to retain human judgment and intuition in analysis.
Called Explorable Visual Analytics (EVA), the tool uses a computer architecture that enables analysts to explore raw data through dynamic visualizations with minimal time delay. The goal is to enable users to make sense of "high-dimensional" data, or data with many parameters. The bulk of the data can reside in an external network, while EVA downloads to the user's computer only the portion being analyzed.
The data-processing pipeline includes pre-processing and caching of data on servers, compressing data to limit the use of communications bandwidth, and caching data on the client computer for better responsiveness. "We are able to give users the illusion they are working with all of a massive dataset while actually sending only a small proportion of the data to the client," says CMU researcher Amir Yahyavi.
He says EVA's rapid response enables users to quickly explore different parameters and examine them using the most appropriate types of graphics. EVA also helps in communicating findings from the data so users can share their conclusions and the process used to reach them.
From Carnegie Mellon News (PA)
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