Steering data quality with visual analytics: The complexity challenge

VAST Challenge 2012: Visual analytics for big data

The 2012 Visual Analytics Science and Technology (VAST) Challenge posed two challenge problems for participants to solve using a combination of visual analytics software and their own analytic reasoning abilities. Challenge 1 (C1) involved visualizing the network health of the fictitious Bank of Money to provide situation awareness and identify emerging trends that could signify network issues....

Big Data Visual Analytics

A Visual Analytics Approach for Station-Based Air Quality Data

With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist...

Scale and complexity in visual analytics

This article is a product of a workshop on the Future of Visual Analytics, held in Washington, DC on 4 March 2009. Workshop attendees included representatives from the visual analytics research community across government, industry and academia. The goal of the workshop, and the resulting papers, was to reflect on the first 5 years of the visual analytics enterprise and propose research challen...

Dynamic Visual Analytics—Facing the Real-Time Challenge

Modern communication infrastructures enable more and more information to be available in real-time. While this has proven to be useful for very targeted pieces of information, the human capability to process larger quantities of mostly textual information is definitely limited. Dynamic visual analytics has the potential to circumvent this real-time information overload by combining incremental ...