Designing Guidelines for Visual Analytics System to Augment Organizational Analytics

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This is my work on using visual analytic

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Designing Guidelines for Visual Analytics System to Augment Organizational Analytics

  1. 1. Designing Visual Analytics Systems for OrganizationalEnvironments<br />A Framework and Its Guidelines<br />Xiaoyu Wang<br />Research Associate at UNC Charlotte<br />Visiting Research Scientist at PARC<br />
  2. 2. Motivation<br />GTDVis<br />IRSV<br />What’s a systematic approach to design a user-centered visual analytics system in organizational environment? <br />Taste<br />Visual Analytics Systems<br />Evaluations and Statistical Analysis<br />Familiarity with Domain Users<br />Computation and Automation<br />Knowledge Management and Organizational learning<br />OpsVis<br />
  3. 3. Two-stage Visual Analytics Design Framework<br />Visual<br />Analytics<br />System <br />
  4. 4. Two-step Research Progression<br />Second step:<br />Top-down approach to create design framework that encapsulate the knowledge gained<br />Utilize existing systems for external evidentsto verify and validate the framework<br />Apply the framework to further design and research practices<br />Resulted in A Two-stage Framework for Designing Visual Analytics System in Organizational Environment(to appear in IEEE VAST 2011 )<br />First step:<br />Summarize design knowledge learnt from all my previous research activities<br />Identify similarities and unique of each analytical domains and system design correspondingly<br />Understand the analytical workflows<br />Resulted in guidelines this paper<br />
  5. 5. Collaborators and Settings<br />Bridge Management Project<br />Team: The US Department of Transportation & Civil Engineering Department<br />Scope: Research on techniques for innovative bridge maintenance planning process<br />Document Management Project<br />Team: Palo Alto Research Center & Xerox Corporation<br />Scope: Research on efficient visual abstraction for recalling and managing personal document activities<br />Network Operation Management Project<br />Team: Microsoft Research & Microsoft Cloud Service Team<br />Scope: Research on effective methods for monitoring and responding to cloud service<br />
  6. 6. Observation and Design stage<br />Observation and Design Stage <br />Domain Characterization and Analysis Generalization<br />Domain Observation and Analysis<br />Formative Evaluation<br />Evaluation Metrics<br />(key specifications for assessing the system)<br />Fail<br />Visual Analytics System<br />Summative <br />Evaluation<br />Analysis Encapsulation and Visual Encoding<br />Domain Analysis Dissemination<br />Formative Evaluation<br />Design Artifacts Specification<br />Interaction Specification<br />Visualization Specification<br />Alternative Visualization/Interaction Combinations<br />
  7. 7. Observation and Analysis<br />Formative Evaluation<br />Evaluation Metrics<br />
  8. 8. Example---U.S. DOT: Domain Characterization<br />
  9. 9. Domain Analysis Generalization<br />Bridge the gap between high-level design concepts and fine-grain implementation of such concepts <br />
  10. 10. Design Artifacts and Specification<br />Visual Analytics System<br />Key Actionable Knowledge<br />Visualization and Interaction Specifications<br />Common Task Activities<br />Key Actionable Knowledge<br /><ul><li>Personalized content and information
  11. 11. Easy ‘slice and dice’ information and direct content exploration
  12. 12. Examine and depict information from multiple aspects
  13. 13. Make sense of significant data patterns and trends
  14. 14. Deliver contents in straightforward representation
  15. 15. Enable facet filtering for information personalization
  16. 16. Interactive content exploration and filtering
  17. 17. (Optional) Employ sophisticated data structures
  18. 18. Personalized content and information
  19. 19. Easy ‘slice and dice’ information and direct content exploration
  20. 20. Examine and depict information from multiple aspects
  21. 21. Make sense of significant data patterns and trends </li></ul>Domain Analysis Dissemination and Transformation<br />Formative Evaluation<br />Content Filtering and Customization<br />Interaction Specification<br />Visualization Specification<br /><ul><li>Create hypothesis based on analysis
  22. 22. Identify evidence that supports both thesis and antithesis
  23. 23. Depict information from multiple aspects
  24. 24. Annotate evidence with clear statements
  25. 25. Group evidence with reasoning logic
  26. 26. Allow evidence collection and annotation
  27. 27. Support storytelling and enable interactive grouping of the evidence with users’ reasoning logic
  28. 28. (Optional) Trace interactions and system usage for future automation
  29. 29. Create hypothesis based on analysis
  30. 30. Identify evidence that supports both thesis and antithesis
  31. 31. Depict information from multiple aspects
  32. 32. Annotate evidence with clear statements
  33. 33. Group evidence with reasoning logic</li></ul>Alternative Visualization/Interaction Combinations<br />Evidence Collection and Hypothesis Generation<br />
  34. 34. Summary of Designing VA for General Analysis<br />Visual<br />Analytics<br />System <br />
  35. 35. User-centric Refinement<br />Summative <br />Evaluation<br />Pass<br />User-centric Refinement stage II<br />Refine Analysis Focuses<br />Customize Visualization Combination<br />Update Data Model<br />Analysis Evaluation and Knowledge Validation<br />Documentation<br />Support<br />Installation<br />System Deployment and User Training<br />Usage Pattern Analysis <br />and Customization<br />
  36. 36. Usage Pattern and Customization Step<br />Customize Visualization Combination<br />Refine Analysis Focuses<br />Update Data Model<br />System Deployment and User Training<br />Analysis Evaluation and Knowledge Validation<br />
  37. 37. Interaction Logging and Capturing User’s Analysis Provenance<br />* Empirical study can be found in Dou et al. (2010) : “Comparing different levels of interaction constraints for deriving visual problem isomorphs”<br />
  38. 38. Interaction Logging Example<br />
  39. 39. Usage Pattern and Customization Step<br />Customize Visualization Combination<br />Refine Analysis Focuses<br />Update Data Model<br />System Deployment and User Training<br />Analysis Evaluation and Knowledge Validation<br />
  40. 40. Annotation Tracking and Content Sharing<br />
  41. 41. Annotation Example: DOT Web<br />Instant Sharing with Colleagues<br />Multiple Evidence Collections<br />Freeform Selection and Graph Connection<br />Detailed Annotation<br />
  42. 42. Summary of Designing VA for Individual Analysis processes<br />Visual<br />Analytics<br />System <br />
  43. 43. Contributions<br />Constructed a two-stage visual analytics design framework to incorporate both general domain analytical process and individual analysis approaches<br />Generalize domain analytical workflows to present high-level problem-solving direction<br />Bridge the gap between high-level design concepts and fine-grain implementation of such concepts <br />Augment organizational information analyses through modeling domain users’ reasoning approaches<br />
  44. 44. Impacts<br />
  45. 45. Future Work<br />Continue working interactive learning from domain users’ interaction logs<br />Machine learning<br />Reactive (emotion) visualization<br />Contribute to the evaluation foundation of visual analytics<br />Create standard evaluation metrics<br />Identify key measures for assessing knowledge-gain through using visual analytics<br />
  46. 46. Questions<br />Xiaoyu Wang<br />Probably on Skype Now..<br />Charlotte Visualization Center<br />http://webpages.uncc.edu/~xwang25<br />xwang25@uncc.edu<br />
  47. 47. Case: Design Artifacts and Specification<br />
  48. 48. Summary of Observation and Design stage<br />Domain observation and Analysis<br />Generalization of Domain Analysis Processes<br />Elements needs to be considered during observation and domain characterization<br />Evaluation Metrics that are useful throughout the design as an assessment to the function<br />Design artifacts<br />Actionable knowledge is a fine-grain items to analytically examine the domain’s general analytical workflow<br />Disseminate general task activities into design artifacts through actionable knowledge<br />Design considerations that are generated based on design artifacts.<br />Visual analytics design needs to follow these artifacts<br />
  49. 49. Example---Xerox: Domain Characterization<br />

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