1 Visual Analytics Techniques that Enable Knowledge Discovery: Detect the Expected and Discover the Unexpected Jim J. Thomas Director, National Visualization and Analytics Center AAAS Fellow, Pacific Northwest National Laboratory Fellow 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 science Discussion of selected existing systems and technologies Common characteristics enabling knowledge discovery Top 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 Unexpected TM 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 ( ) 5

What is not visual analytics? Large graph structure with no labels Heat map with no labels Search and retrieval systems Chart with no interaction Image with no semantic interpretation Stand alone image that does not tell a story 6

The Landscape of Visualization Science Publications from IEEE VisWeek, 2006, 2007, 2008 using IN-SPIRE Visual Analytics Tool Each dot is an published science article, full text

Systems Considered: IN-SPIRE JIGSAW - John Stasko, Carsten Görg, and Zhicheng Liu, “Jigsaw: Supporting Investigative Analysis through Interactive Visualization,” Information Visualization, vol. 7, no. 2, pp , Palgrave Magellan, 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) 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): 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 Relationship Scale independent representations, whole and parts at same time at multiple levels of abstraction, often linked 9

Whole - Part Relationship 10

Relationship Discovery Explore 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 hypothesis Evidence collection, management, and matching to hypothesis Tailor views/displays for thematic/hypothesis focus of interest Often suggestive of predictions enabling proactive thinking 13

Multiple Data Types Supports multiple data types: structured/unstructured text Imagery/video, cyber Systems of either data type or application specific 14

Temporal Views and Interactions Most analytics situations involve time, pace, velocity Group segments of thoughts by time Compare time segments Often combined with geospatial 15

Reasoning Workspace 16 Workspace to construct logic and illustrate reasoning Flexible spatial view of reasoning: stories Stu Card, PARC

Grouping and Outlier Detection Form groups of thought/data Labels and annotation Compare groupings Find small groups or outliers 17

Labeling Critically important, Dynamic in scope, number labels, size, color Positioning Almost everything has labels Labels tell semantic meaning 18

Multiple Linked Views Temporal, geospatial, theme, cluster, list views with association linkages between views 19

Multiple Linked Views Temporal, 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

Reporting Capture display segments in graph modes for putting in reports, PPT etc Capture reasoning segments of analytic results Capture animations 22

Engaging Interaction GreenGrid video 23

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 thinking New visual paradigms that deal with scale, multi- type, dynamic streaming temporal data flows Data, Information and Knowledge Representation Collaborative Predictive/Proactive Visual Analytics Visual Analytic Method Capture and Reuse

27 Top Ten Challenges Within Visual Analytics Dissemination and Communication Visual Temporal Analytics Validation/verification with test datasets openly available Delivering short-term products while keeping the long view Interoperability interfaces and standards: multiple VAC suites of tools 27

Conclusions Visual Analytics is an opportunity worth considering Practice of Interdisciplinary Science is required Broadly applies to many aspects of society For each of you: 28 The best is yet to come…