The covert nature of sex trafficking provides a significant barrier to generating large-scale, data-driven insights to inform law enforcement, policy and social work. We leverage massive deep web data (collected globally from leading commercial sex websites) in tandem with a novel machine learning framework to unmask suspicious recruitment-to-sales pathways, thereby providing the first global network view of trafficking risk in commercial sex supply chains.
Graph Mapping: Multi-Scale Community Visualization of Massive Graph Data
Graph visualizations increase the perception of entity relationships in a network. However, as graph size and density increases, readability rapidly diminishes. In this cover article for the July 2017 edition of the Information Visualization journal, we present an end-to-end, tile-based visual analytic approach called graph mapping that utilizes cluster computing to turn large-scale graph (node-link) data into interactive visualizations in modern web browsers.
Multi-Scale Community Visualization of Massive Graph Data
Graph visualizations increase the perception of entity relationships in a network. However, as graph size and density increases, readability rapidly diminishes. In this paper, we present a tile-based visual analytic approach that leverages cluster computing to create large-scale, interactive graph visualizations in modern web browsers. Our approach is optimized for analyzing community structure and relationships.
Global to Local Pattern of Life Analysis with Tile-Based Visual Analytics
In this paper, we present in-progress work on applications of tile-based visual analytics (TBVA) to population pattern of life analysis and geo-temporal event detection. TBVA uses multiresolution data tiling, analytics and layered high-fidelity visualization to enable interactive multi-scale analysis of billions of records in modern web browsers through techniques made familiar by online map services.
Influent: Scalable Transaction Flow Analysis with Entity-Relationship Graphs
In this paper, we present in-progress work on “Influent,” a graph analysis tool that enables an intelligence analyst to visually and interactively “follow the money” or other transaction flow. Summary visualizations of transactional patterns and entity characteristics, a left-to-right semantic flow layout, interactive link expansion and hierarchical entity clustering enable Influent to operate effectively at scale with millions of entities and hundreds of millions of transactions, with larger data sets in progress.
Tile Based Visual Analytics for Twitter Big Data Exploratory Analysis
The widespread use and adoption of web-based geo maps have provided a familiar set of interactions for exploring extremely large geo data spaces and can be applied to similarly large abstract data spaces. Building on these techniques, a tile based visual analytics system (TBVA) was developed that demonstrates interactive visualization for a one billion point Twitter dataset.
Louvain Clustering for Big Data Graph Visual Analytics
Interactive Data Exploration with "Big Data Tukey Plots"
Our goal is to develop new automated tools to produce effective raw data characterization on extremely large datasets. This paper reports on the development of a web based interactive scatter plot prototype that uses tile-based rendering similar to geographic maps and interaction paradigms.
Why AI Can't Save the World (Yet), and How Humans Can Help
Analysts tasked with solving social problems like human trafficking rely on long-established processes and field experience. Broad artificial intelligence solutions to these problems have good intentions, but they often omit nuance and context that analysts bring. In this talk, we present Uncharted’s approach to designing “augmented intelligence” tools for global resilience that let both humans and machines do what they do best.
Text Analytics and New Visualization Techniques
Text analytics have significantly advanced with techniques such as entity extraction and characterization, topic and opinion analysis, and sentiment and emotion extraction. But the visualization of text has advanced much more slowly. Recent visualization techniques for text, however, are providing new capabilities. In this talk we offer an overview of these new ways to organize massive volumes of text, characterize subjects, score synopses, and skim through a lot of documents. Together, these techniques can improve workflows for users focused on documents.
Value in the Details: Understanding Data Through Visual Exploration
Analysts and data scientists work with large amounts of data, but the common approach, which dates back 20 years, is to roll up all the data into summary tables or charts, resulting in loss of detail. In contrast, direct visual exploratory analysis of massive amounts of raw data can yield insights that are otherwise overlooked. Here we highlight high-density visualizations that directly plot hundreds of millions of data points for applications such as market opportunity, periodicity analysis, and anomaly identification.
Untangling Influence and Desire: Visual Analysis of Massive Graph Data
This session demonstrates using open source tools and techniques for visually exploring massive node-link graphs in a web browser by visualizing all the data. Seeing all the data reveals informative patterns and provides important context to understanding insights. Examples will highlight large-scale graph analysis of social networks, customer purchase history, and healthcare industry data.
Highly Scalable Tile-Based Visualization for Exploratory Data Analysis
The open source Aperture Tiles provides interactive data exploration with continuous zooming on large scale datasets. By leveraging big data software stacks Hadoop, Apache Spark and HBase for distributed computation and storage, the technique can scale to data sets with billions of data points or more. In this talk we review the Tile-based visual analytic methodology, challenges and highlight some example applications.
Visualizing Big Graphs and Social Networks
Big graphs are difficult to visualize because the scale of the data quickly results in complex network diagrams that can be difficult to decipher and have complicated interfaces with hundreds of options. Instead, we start by understanding what valuable nuggets we are trying to learn from this data, such as “Who’s connected to both me and my competition?” and “Did these people meet?” Starting from the question, we can then simplify the requirements and design effective visualizations that fit the problem. We will look at some mini-case studies and demos (e.g. Twitter correlation network, charity network) and some of the issues