Syndicate 4: Information Visualization

Information Visualizationfor CounterTerror Intelligence

David Zeltzer Fraunhofer

Center for Research in Computer Graphics, Inc.Providence RI

Information Visualization Needs

for Intelligence and CounterTerror

N/X Meeting 10-11 March, 2003

Penn State University

Syndicate 4: Information Visualization• Massive Military Data Fusion and Visualisation:

Users Talk with Developers- Workshop IST-036/RWS-005- 10-13 September 2002- Halden NORWAY

• Syndicate 4 Members– Denis Gouin– Zack Jacobson– “Kesh” Kesavadas– Hans-Joachim Kolb– Vincent Taylor– Johan Carsten Thiis– David Zeltzer

Overview

• Syndicate 4 Approach

• Visualization Reference Model

• Counter Terror Intel Requirements

• Capabilities and Technologies

Halden Syndicate 4 Approach

• Information Visualization- How to present “non-physical” information with no

straightforward mapping to 3D metaphor?


• Apply to Specific Domains of Interest to NATO- Counterterror Intelligence

- Requirements

- Functionalities and technologies

• Indicate R&D Directions- Rate technology maturity

- Encapsulate in matrix form

Overview



• Counter Terror Intel Requirements


Visualization Reference Model

Data Process Presentation Multimedia Displays

Task LevelHMI

Make Process Output"Visible"

Peripheral H/W andS/W Controllers

"Invisible"Computer

InteractionTokens

Process Control& Queries

DataRequests

• Similar to VisTG model, Martin Taylor• Focus on Computational Engines for Data Analysis and Presentation

Visualization: The “3D Metaphor”• 2D Visualization Extremely Effective

• Decades-long Effort in Scientific Visualizaton Has Resulted in 3D Visualizaton Toolkits

- Toolkits work well for problems that map to 3D geometry + time and a few other parameters

- 3D metaphor

AVS/ExpressAdvanced Visual Systems, Inc.

Vis5DUniversity of Wisconsin

nScopeFourth Planet, Inc.

Visualization: The “Hard” Problems• Limits of the 3D Metaphor

- Is the 3D metaphor the key to understanding?- How can many, varied kinds of information be visually fused,

coherently displayed and manipulated?- How can information qualities be portrayed?

» uncertainty » timeliness» accuracy» . . .

- How can abstract, multi-dimensional data sources be displayed?» financial» proteiomics» counter terror intel» . . .

Visualization:A Multi-Disciplinary Look• What Is an “Information Workplace”?• How Can the Design of Visualization Tools Make

Use of Knowledge About Human Perception and Cognition?

• How Can Human Perceptual and Cognitive Talents Be Enhanced and Amplified Through Visualization?

• How Can the Long and Rich History of Visualization in the Arts Be Exploited in the Information Age?

• Much Visualization Algorithm Automation — What About Automation of HMI Components?

• The Only Way to Do That Is by Integrating Knowledge About

- You,

- Your situation(s), and your

- Tasks and decision(s)

“Show me what I need to know, when I need to know it!”

Ontology-Based Computing

• 21st Century Approach to Human-Centered Computing

• Integrate Human-Centered Knowledge into Computation- Who am I?- Where am I?

» on the planet?» on the network?

- What am I trying to do?- What do I need to know?- What resources are available?- What don’t I know?- Am I fatigued? Stressed? Working too hard?

DecisionFocus

IncomingData

Domain Abstractions

DecisionRequirements

DomainOntology

Agents Monitor& Alert

AutomaticTailoring

NarrativeTheory

PresentationManager

InteractionCycle

• View Control• Interactive

Commands&

Queries

Task LevelMultimodal

HMI InteractionDialog

Classify, Prioritize,Associate Incoming

Data

AssociationEngine

MultimediaDisplays

Decision-Centered Visualization

Interactive Visualization

KnowledgeComponents

Entity Knowledge

Task and Decision Knowledge

Overview



• CounterTerror Intel Requirements


CounterTerror Intel Requirements“Before we can connect the dots, we first

have to collect the dots.”- Technology Review,

March 2003

• Intel Data Must Be- Gathered- Analyzed- Presented

• Intel Data Collection and Sensor Technologies Outside Syndicate 4 Scope

• Intel Data Sources Identified

• Intel Data Gathering and Analysis Is Controversial in Democratic Societies- DARPA Total Information Awareness

• Who Are We Tracking?

• How Much Is Too Much?

CounterTerror Intel Data Sources

• Communications- Email, Phone, FAX, Radio, Video, . . .

• Open Sources- Newspapers, WWW, Newsgroups, TV, . . .

• Commercial Transactions- Individuals- Organizations

• Behaviors- Individuals- Organizations

CounterTerror Intel Data Analysis• Data Magnitude Requires Focus on Suspect Popul

ations• Step 1: Feature Recognition

- Far Too Much Raw Data to Process- Data reduction = (Feature Recognition Filter)

• Content Analysis - Arbitrarily complex algorithms and software

» Automation» Human-in-the-loop

- Link analysis- Data mining- Behavior analysis

• Presentation- Identify visualization and HMI issues

What Are We Looking For?• Are We Trying to Find Patterns Among

Suspect Individuals and Organizations?- Surveillance restricted to suspect populations- Look for target (known?) patterns

• Are We Trying to Identify Suspects From Anamalous Patterns?

- Watch everyone- Look for target(?) patterns- Look for anomalies- What’s anomalous?

Overview



• CounterTerror Intel Requirements


CounterTerror Intel Data Analysis

• Feature Recognition- Communications- Open Sources- Commercial Transactions- Behaviors

• Link Analysis• Data Mining• Behavior Analysis

Feature Recognition and Communications• Email, Phone, FAX, Radio, Video

- Many easily recognized parameters

» Source, destination(s), length, encrypted(?), language, subject field, attachments, routing, etc.

- Content analysis

» Textual concept recognition• High in some languages

• Low for multilingual

• High OCR

• High speech recognition

» Low image and video feature recognition

» Low intent recognition

Visualization of Communication Channels Over Time

Feature Recognition and Open Sources

• Newspapers, WWW, Newsgroups, TV, . . .

• Domain of Discourse Constrained by Context- High Concept Recognition Technologies

- NL concept recognition technologies

- NL paraphrasing

• Low Intent Recognition Technologies

Visualization of Concepts in the Nixon-Watergate Transcripts

Feature Recognition and Commercial Transactions

• Transaction Signatures- Customer ID

- Credit card #

- Product(s) purchased

- Amount of product purchased

- Purchasing frequency and history

- . . .

• Data Sources- All signature parameters maintained by merchants

- Subject to data mining

Feature Recognition and Behaviors• Scope

- Data magnitude requires focus on suspect populations

- Suspect population

• Behavior Signatures- Phone calls

» Recipient and locations- Travel- Residence- Biographical data- . . .

• Data Sources- Current law enforcement surveillance methodologies

Counterterror Intel Analysis

• Feature Recognition- Communications- Open Sources- Commercial Transactions- Behaviors

• Link Analysis• Data Mining• Behavior Analysis

Link Analysis• Find Patterns in Recognized Features

- Relations among people, organizations, events, incidents, behaviors, locations

• Some Tools Available- Automated

- Human-in-the-loop visualization

• Medium Technology Maturity

• Both Automated and Human-in-the-Loop Link Analysis Tools Require Further R&D Including Visualization and HMI

Mapping al-Quaedi v1.0

Example Link Analysis+

• NORA™ - Non-Obvious Relationship Awareness ™

- Systems Research & Development

- http://www.srdnet.com/

- Commercial fraud detection now in use by FBI and . . .

• NORA™ uses SRD's Entity Resolution™ Technology to Cross-reference Databases and Identify Potentially Alarming Non-obvious Relationships Among and Between Individuals and Companies

Are Humans-in-the-Loop Really Necessary?

Data Mining• Search and Exploit (Legacy?) Databases

- Recognized features

- Others . . .

• Mining Structured Data- E.g., commercial transaction data

- Off-the-shelf technologies available but difficult to use

- High maturity but visualization and HMI development required

• Mining Unstructured Data- Low maturity

- Data representation and association, automation tools, HMI and visualization require major R&D

Behavior Analysis

• Compare Events With ‘Normal’ (Baseline) Information Stored in a Knowledge Base

• Scope- Suspect entitities

• Low technology maturity- Many components available but major integration

engineering required- Robust and reliable monitoring technology not available

» Prohibitively high false alarm rate» Human-in-the-loop signal detection » Visualization and HMI R&D

Analysis of Vessel Behavior

• Scope- Track known

entities

• Behavior Baselines

• Filter- Source

- Destination

- Cargo

- Time

• Subject to Vagaries of International Commerce

Behavior Analysis (cont’d)• Objective Distributed Technology

- Regional, local, on-site, transportable

DataBase

KnowledgeBase

Agents Monitor &Alert

Humans Monitorand Alert

VisualizationHMI

SuspectPopulation

DataBehavior Baselines

Summary

• Link Analysis and Data Mining Are “Low Hanging Fruit”

- Technologies “almost there” and potentially most productive in generating useful intelligence

- Technology components exist but visualization and HMI are poor

- Most difficult challenge is algorithm “scaling”

- Technologies are evolving and may be influenced by N/X working group

Questions?

Documents

Syndicate 4: Information Visualization