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Graph Mapping Featured in Leading InfoVis Research Journal

Novel interactive visualization technique for massive graph data reveals hierarchical community structures and entity relationships in large-scale networks

Michael Crouch
August 24, 2017

An Uncharted research product for the visual analysis of massive graph data is featured in the pages and on the cover of the latest issue of Information Visualization, a leading peer-reviewed journal on the study and application of information visualization.

Virtual Reality Taxi Trips in NYC

Interactive 3D Visualization of High-Density Plots

Richard Brath, Luke Corrigall
October 18, 2016

What if you could move freely through Manhattan and watch every licensed taxi pick up and drop off every one of its passengers at the same time? What movement patterns would you see? That’s what we aimed to find out by extending our Salt library to plot high-density New York City traffic data in three dimensions.

TorFlow

Data Flow in the Tor Network

Chris Dickson and Kevin Birk
January 17, 2016

The Tor project is an open network for anonymous communication over the internet. Tor routes users’ internet traffic through a series of volunteer-run relay nodes to conceal its origin and destination from potential surveillance or censorship. While Tor is built for anonymity, the structure of the network and locations of many of the relay nodes is open.

Introducing Uncharted Salt

Open source, multi-scale big data visualization

Sean McIntyre
December 22, 2015

One of the questions we’ve spent a lot of time pondering over the years is a deceptively simple one - How do we visualize billions of data points? To understand why this is a difficult question to answer, let’s look at two popular approaches.

Continuous Integration with Apache Spark

Sean McIntyre
November 6, 2015

We’ve been writing libraries for Spark at Uncharted for several years, and continuous integration involving the Spark runtime has always been a difficult thing to accomplish. Common approaches, such as creating a Spark context within a standard Scala runtime, can fail to accurately emulate nuances of the distributed Spark environment. I’d like to share a solution we’ve developed for creating a native Spark test environment within TravisCI.