Latest News and Events

The SAMSI-FODAVA Workshop on Interactive Visualization and Analysis of Massive Data will be held on December 10-12, 2012.
Posted: October 02, 2012
The FODAVA Annual Meeting will immediately follow (Dec 12-13) the SAMSI/FODAVA joint workshop at the same location.
Posted: September 05, 2012
Many of the modern data sets such as text and image data can be represented in high-dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from num
Posted: June 30, 2012

Uncertainty-Aware Data Transformations for Collaborative Reasoning

The ability to obtain insight from massive, dynamic, and likely incomplete digital data is absolutely essential to those who collect these data for time-critical decision-making. This research is developing mathematical formulations for the quantification, propagation, and aggregation of such data to support collaborative reasoning using visual means. The fundamental importance of this research is to give analysts a more trustworthy view of data with the consideration and incorporation of uncertainty due to data transformations and the propagation of that information to the reasoning stage.

The process of visual analytics can be viewed as a series of data and visual transformations. The investigators are studying the transformations that help analysts discover structural relationships in large data repositories. These relationships can express aspects of the data such as membership, inclusion, overlapping, and bijective mappings between different data elements. Both data collection and transformation steps, however, incur certain degrees of uncertainty, some due to the transformations, and others inherently present in the source data. Uncertainty can degrade the quality of the information. Understanding and quantifying this uncertainty is crucial to collaborate reasoning, which is the product of the aggregation of confidence levels from several analysts. This research models such transformations and uncertainties as a Bayesian network. Nodes in this network represent the transformations and their associated uncertainty, and also represent the confidence levels of different collaborating analysts. Aggregation of uncertainty between transformations and among analysts is modeled using propagation laws and conditional probabilities. Such a model results in a visual analytics framework providing an explicit representation of uncertainty to enable analysts to assign confidence levels to the gained insight. This framework effectively helps alleviate the complexity of sense-making and visual reasoning.