可 视 化 与 可 视 计 算 概 论. Introduction to Visualization and Visual Computing 袁 晓 如 北 京 大 学

Transcription

1 可 视 化 与 可 视 计 算 概 论 Introduction to Visualization and Visual Computing 袁 晓 如 北 京 大 学

2 2 Visual Analytics Adapted from Jim Thomas s slides

3 3 Visual Analytics Definition Visual Analytics is the science of analytics 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

4 The Homeland Security Challenge 4 Diverse, multiple threats Complex, interrelated vulnerabilities Low tolerance for false alarms Scale and Diversity of information needed Privacy assurance Deception

5 5 U.S. Ports of Entry

6 6 Border Security Examples

7 Examples Demonstrating Need 7 Information Logistics (Data Sciences) (Data capture -> massive rate) Adaptive Middleware/Data Concierge and Universal Parsing Agent

8 Examples Demonstrating Need 8 Towards Predictive Analytics discovery of the unexpected through Hypothesis/Scenario-based Analytics (hypothesis testing IN-SPIRE)

9 Examples Demonstrating Need 9 Changing Nature of information Structure: Temporal, dynamically changing relationships, determination of intenet (DC Sniper & ThemeRiver)

10 Examples Demonstrating Need 10 Information synthesis while preserving security and privacy Data Signatures that are semantic and scale

11 11 Is this you?

12 12 Is this you? Or are you fast on a solution and this happens to you?

13 13 Or have you been deceived?

14 Examples Demonstrating Need 14 Walk up useable Immersion into my context space Look at Old information from new perspectives with new experimental data Capture analytics process for new uses within different situation Real time analytics Policy/jurisdiction sensitivities, human and sensor data, models, financial data, and impact to the public Visual communication to tell a story

15 15 New requirements Summary Volume of data, orders of magnitude larger and different level of abstraction Complexity of information spaces into very high dimensions, 200 the norm Information in all media types: text, imagery, video, voice, web, sensor data Time and temporal dynamics fundamentally change the approach Spatial, yet non-spatial abstract data Multiple ontologies, languages, cultures For homeland security and science we now turn to dataintensive visual analytics

16 16 The Good News We seldom need all informatin on any topic We can learn new knowledge from others We can share with others in microseconds We can, if approved within security and privacy policies, have access to landscapes of information However, this volume of information poses challenges and opportunities, scale changes everything

17 National Visualization and Analytics Centers in U.S. 17

18 18 Technology Evolution Computer graphics 1960s with current emphasis on entertainment and realism Scientific Visualization: 1980s with emphasis on physics based problems, animation, simulation, VR/AR Information Visualization: 1990s with emphasis on lines, geospatial, hierarchical, and text visualization Visual Analytics: 2000s emphasis on enhancing and stimulating creative analytical thinking to Detect the expected and discover the unexpected

19 Visual Analytics Applies to Many DHS Mission Needs 19

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22 22 Core concept

23 23 Visual Abstraction

24 24 Text Visualizations

25 25 IN-SPIRE: Document Viewer

26 26 IN-SPIRE: Analyst Control Control over Focus/Discourse Dynamic Perspective

27 27 IN-SPIRE: Live Data Streams

28 IN-SPIRE: Hypothesis Assessment Over Time 28

29 IN-SPIRE: Improved Evidence Handling 29

30 IN-SPIRE: Correlation Understanding 30

31 IN-SPIRE: Queries, Retrieval Interaction 31

32 IN-SPIRE: Repeatable retrieval/triage Strategies 32

33 33 IN-SPIRE: Summary

34 Starlight Information Visualization System 34

35 35

36 36 Uses Today

37 37 Overview of the R&D agenda

38 38 Analytical Reasoning

39 Recommendations: Analytical Reasoning 39

40 Visual Representations & Interaction Techniques 40

41 Recommendations: Visual Representations & Interaction Techniques 41

42 Data Representations & Transformations 42

43 Recommendations: Data Representations & Transformations 43

44 Production, Presentation & Dissemination 44

45 Recommendations: Production, Presentation & Dissemination 45

46 Moving Research into Practice 46

47 Recommendations: Moving Research into Practice 47

48 Recommendations: Positioning for Enduring Success 48

49 49 Call for Action Visual analytics is a grand challenge in science Progress measured by New visual analytic techniques are being transitioned into analytical use A vibrant and growing community of practice for visual analytics researchers and engineers. Implemented through partnerships: Academia Industry Other government agencies National laboratories