1

Visual Analytics Techniques that Enable Knowledge Discovery:

Detect the Expected and Discover the Unexpected

Jim J. ThomasDirector, National Visualization and Analytics Center

AAAS Fellow, Pacific Northwest National Laboratory Fellowhttp://NVAC.pnl.govJim.thomas@pnl.gov

ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery

VAKD '09 Paris, France

Visual Analytics Techniques that Enable Knowledge Discovery

Introduction: what is and is not visual analytics?Landscape of visualization scienceDiscussion of selected existing systems and technologiesCommon characteristics enabling knowledge discoveryTop ten challenges

2

Introduction:History of Graphics and Visualization• 70s to 80s

– CAD/CAM Manufacturing, cars, planes, and chips– 3D, education, animation, medicine, etc.

3

• 80s to 90s– Scientific visualization– Realism, entertainment

• 90s to 2000s– Information visualization– Web and Virtual environments

• 2000s to 2010s– Visual Analytics– Visual/audio analytic appliances

Visual Analytic Collaborations

Detecting the Expected -- Discovering the UnexpectedTM

4

Visual Analytics Definition

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces.

People use visual analytics tools and techniques to Synthesize information and derive insight from massive,

dynamic, ambiguous, and often conflicting data Detect the expected and discover the unexpected Provide timely, defensible, and understandable assessments Communicate assessment effectively for action.

“The beginning of knowledge is the discovery of something we do not understand.” ~Frank Herbert (1920 - 1986) 5

What is not visual analytics?

Large graph structure with no labelsHeat map with no labelsSearch and retrieval systemsChart with no interactionImage with no semantic interpretationStand alone image that does not tell a story

6

The Landscape of Visualization Science

Publications from IEEE VisWeek, 2006, 2007, 2008using IN-SPIRE Visual Analytics Tool

Each dot is an published science article, full text

Systems Considered:IN-SPIRE - http://in-spire.pnl.gov.

JIGSAW - John Stasko, Carsten Görg, and Zhicheng Liu, “Jigsaw: Supporting Investigative Analysis through Interactive Visualization,” Information Visualization, vol. 7, no. 2, pp. 118-132, Palgrave Magellan,

2008.WIREVIZ - Remco Chang, Mohammad Ghoniem, Robert Korsara, William Ribarsky, Jing Yang, Evan Suma, Carolina Ziemkiewicz, Daniel Keim, Agus Sudjianto, IEEE Visual Analytics Science and Technology

(VAST) 2007. GreenGrid - Pak Chung Wong, Kevin Schneider, Patrick Mackey, Harlan Foote, George Chin Jr., Ross Guttromson, Jim Thomas “A Novel Visualization Technique for Electric Power Grid Analytics,” IEEE Transactions on Visualization and Computer Graphics 15(3):410-423.

Scalable Reasoning System - Pike WA, JR Bruce, RL Baddeley, DM Best, L Franklin, RA May, II, DM Rice, RM Riensche, and K Younkin. (2008) "The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics."  In IEEE Symposium on Visual Analytics Science and Technology (VAST).

8

Whole - Part RelationshipScale independent representations, whole and parts at same time at multiple levels of abstraction, often linked

9

Whole - Part Relationship

10

Relationship DiscoveryExplore high dimensional relationships, theme groupings, outlier detection, searching by proximity at multiple scales

11

Relationship Discovery

12

Boolean

By Example

Combined Exploratory and Confirmatory Analytics

Develop and refine hypothesisEvidence collection, management, and matching to hypothesisTailor views/displays for thematic/hypothesis focus of interestOften suggestive of predictions enabling proactive thinking

13

Multiple Data Types

Supports multiple data types: structured/unstructured textImagery/video, cyberSystems of either data type or application specific

14

Temporal Views and InteractionsMost analytics situations involve time, pace, velocityGroup segments of thoughts by timeCompare time segmentsOften combined with geospatial

15

Reasoning Workspace

16

Workspace to construct logic and illustrate reasoningFlexible spatial view of reasoning: stories

Stu Card, PARC

Grouping and Outlier DetectionForm groups of thought/dataLabels and annotationCompare groupingsFind small groups or outliers

17 17

LabelingCritically important, Dynamic in scope, number labels, size, colorPositioningAlmost everything has labelsLabels tell semantic meaning

18

Multiple Linked ViewsTemporal, geospatial, theme, cluster, list views with association linkages between views

19

Multiple Linked ViewsTemporal, geospatial, theme, cluster, list views with association linkages between views

20

Heatmap View(Accounts to Keywords Relationship)

Strings and Beads(Relationships over Time)

Search by Example (Find Similar Accounts)

Keyword Network(Keyword Relationships)

WireViz Video

21

ReportingCapture display segments in graph modes for putting in reports, PPT etcCapture reasoning segments of analytic resultsCapture animations

22

Engaging InteractionGreenGrid video

23

Alberta

North California

Southern

Northern

GreenGrid Video

24

Tested With Known Data and Solutions

25

26

Top Ten Challenges Within Visual Analytics

Human Information Discourse for Discovery—new interaction paradigm based around cognitive aspects of critical thinkingNew visual paradigms that deal with scale, multi-type, dynamic streaming temporal data flowsData, Information and Knowledge RepresentationCollaborative Predictive/Proactive Visual AnalyticsVisual Analytic Method Capture and Reuse

27

Top Ten Challenges Within Visual Analytics

Dissemination and CommunicationVisual Temporal AnalyticsValidation/verification with test datasets openly availableDelivering short-term products while keeping the long viewInteroperability interfaces and standards: multiple VAC suites of tools

27

Conclusions

Visual Analytics is an opportunity worth consideringPractice of Interdisciplinary Science is requiredBroadly applies to many aspects of society For each of you:

28

The best is yet to come…