This is my work on using visual analytic

1. Designing Visual Analytics Systems for OrganizationalEnvironments A Framework and Its Guidelines Xiaoyu Wang Research Associate at UNC Charlotte Visiting Research Scientist at PARC

2. Motivation GTDVis IRSV What’s a systematic approach to design a user-centered visual analytics system in organizational environment? Taste Visual Analytics Systems Evaluations and Statistical Analysis Familiarity with Domain Users Computation and Automation Knowledge Management and Organizational learning OpsVis

3. Two-stage Visual Analytics Design Framework Visual Analytics System

4. Two-step Research Progression Second step: Top-down approach to create design framework that encapsulate the knowledge gained Utilize existing systems for external evidentsto verify and validate the framework Apply the framework to further design and research practices Resulted in A Two-stage Framework for Designing Visual Analytics System in Organizational Environment(to appear in IEEE VAST 2011 ) First step: Summarize design knowledge learnt from all my previous research activities Identify similarities and unique of each analytical domains and system design correspondingly Understand the analytical workflows Resulted in guidelines this paper

5. Collaborators and Settings Bridge Management Project Team: The US Department of Transportation & Civil Engineering Department Scope: Research on techniques for innovative bridge maintenance planning process Document Management Project Team: Palo Alto Research Center & Xerox Corporation Scope: Research on efficient visual abstraction for recalling and managing personal document activities Network Operation Management Project Team: Microsoft Research & Microsoft Cloud Service Team Scope: Research on effective methods for monitoring and responding to cloud service

6. Observation and Design stage Observation and Design Stage Domain Characterization and Analysis Generalization Domain Observation and Analysis Formative Evaluation Evaluation Metrics (key specifications for assessing the system) Fail Visual Analytics System Summative Evaluation Analysis Encapsulation and Visual Encoding Domain Analysis Dissemination Formative Evaluation Design Artifacts Specification Interaction Specification Visualization Specification Alternative Visualization/Interaction Combinations

7. Observation and Analysis Formative Evaluation Evaluation Metrics

8. Example---U.S. DOT: Domain Characterization

9. Domain Analysis Generalization Bridge the gap between high-level design concepts and fine-grain implementation of such concepts

11. Easy ‘slice and dice’ information and direct content exploration

12. Examine and depict information from multiple aspects

13. Make sense of significant data patterns and trends

14. Deliver contents in straightforward representation

15. Enable facet filtering for information personalization

16. Interactive content exploration and filtering

17. (Optional) Employ sophisticated data structures

18. Personalized content and information

19. Easy ‘slice and dice’ information and direct content exploration

20. Examine and depict information from multiple aspects

22. Identify evidence that supports both thesis and antithesis

23. Depict information from multiple aspects

24. Annotate evidence with clear statements

25. Group evidence with reasoning logic

26. Allow evidence collection and annotation

27. Support storytelling and enable interactive grouping of the evidence with users’ reasoning logic

28. (Optional) Trace interactions and system usage for future automation

29. Create hypothesis based on analysis

30. Identify evidence that supports both thesis and antithesis

31. Depict information from multiple aspects

32. Annotate evidence with clear statements

33. Group evidence with reasoning logicAlternative Visualization/Interaction Combinations Evidence Collection and Hypothesis Generation

34. Summary of Designing VA for General Analysis Visual Analytics System

35. User-centric Refinement Summative Evaluation Pass User-centric Refinement stage II Refine Analysis Focuses Customize Visualization Combination Update Data Model Analysis Evaluation and Knowledge Validation Documentation Support Installation System Deployment and User Training Usage Pattern Analysis and Customization

36. Usage Pattern and Customization Step Customize Visualization Combination Refine Analysis Focuses Update Data Model System Deployment and User Training Analysis Evaluation and Knowledge Validation

37. Interaction Logging and Capturing User’s Analysis Provenance * Empirical study can be found in Dou et al. (2010) : “Comparing different levels of interaction constraints for deriving visual problem isomorphs”

38. Interaction Logging Example

39. Usage Pattern and Customization Step Customize Visualization Combination Refine Analysis Focuses Update Data Model System Deployment and User Training Analysis Evaluation and Knowledge Validation

40. Annotation Tracking and Content Sharing

41. Annotation Example: DOT Web Instant Sharing with Colleagues Multiple Evidence Collections Freeform Selection and Graph Connection Detailed Annotation

42. Summary of Designing VA for Individual Analysis processes Visual Analytics System

43. Contributions Constructed a two-stage visual analytics design framework to incorporate both general domain analytical process and individual analysis approaches Generalize domain analytical workflows to present high-level problem-solving direction Bridge the gap between high-level design concepts and fine-grain implementation of such concepts Augment organizational information analyses through modeling domain users’ reasoning approaches

44. Impacts

45. Future Work Continue working interactive learning from domain users’ interaction logs Machine learning Reactive (emotion) visualization Contribute to the evaluation foundation of visual analytics Create standard evaluation metrics Identify key measures for assessing knowledge-gain through using visual analytics

46. Questions Xiaoyu Wang Probably on Skype Now.. Charlotte Visualization Center http://webpages.uncc.edu/~xwang25 xwang25@uncc.edu

47. Case: Design Artifacts and Specification

48. Summary of Observation and Design stage Domain observation and Analysis Generalization of Domain Analysis Processes Elements needs to be considered during observation and domain characterization Evaluation Metrics that are useful throughout the design as an assessment to the function Design artifacts Actionable knowledge is a fine-grain items to analytically examine the domain’s general analytical workflow Disseminate general task activities into design artifacts through actionable knowledge Design considerations that are generated based on design artifacts. Visual analytics design needs to follow these artifacts

49. Example---Xerox: Domain Characterization