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Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
Designing Guidelines for Visual Analytics System to Augment Organizational Analytics
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Designing Guidelines for Visual Analytics System to Augment Organizational Analytics

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This is my work on using visual analytic

This is my work on using visual analytic

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  • Over the years, I have fun working on several visual analytics system design, participated in design user evaluations and studies, this gives me the idea of standard to evaluate a system, and a design and some exposures to machine learning and robotic, taught me not only thinking computations and automation, but also exploring possibilities to incorporate human in the process with computer. and finally, quite some effort in learning organization and other stuff that isn’t typically widely studied in computer science, worked with ontology etc. Where these lead me. The diversified visual analytics design and other analytical approaches lead to the question about what’s the general framework that can not only encapsulate domain users’ analytical processes, but also allow them to customize it.
  • This is the high-level process Need to work on the Goals!
  • context analysis is a useful method to analyze the environment in which a organization operates. On a broader scope, context analysis provides a constrain to ensure that all factors that may affect the usability of a product are considered. It also helps to ensure that user-based evaluation produces valid results, by specifying how important factors are handled in an evaluation, and by defining how well the evaluation reflects real world.The main goal for a context analysis in the design process is to analyze the organizational environments in order to acquire an overall characteristic of the domain. In developing a visual analytics system for organizational environments, the context analysis has been narrowed to understand the technical, analysis and collaboration settings where the visual analytics systems will be used.
  • Analytical methods to support a trade studyTrade studies are essentially decision-making exercises. In the FAA Systems Handbook (FAA 2004), the decision analysis matrix (aka Pugh's method) is suggested to support the activities, but this method can not support uncertainty, Explain a bit about what actionable knowledge is in actual
  • Work on objectives!!!!
  • While visual representations can aid problem solving significantly on their own, they gain even more power to model a problem when interaction is introduced. Interaction is increasingly seen as central to the process of reasoning with visualization
  • While visual representations can aid problem solving significantly on their own, they gain even more power to model a problem when interaction is introduced. Interaction is increasingly seen as central to the process of reasoning with visualization
  • Need Changes
  • Transcript

    • 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. 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. Two-stage Visual Analytics Design Framework<br />Visual<br />Analytics<br />System <br />
    • 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. Collaborators and Settings<br />Bridge Management Project<br />Team: The US Department of Transportation &amp; Civil Engineering Department<br />Scope: Research on techniques for innovative bridge maintenance planning process<br />Document Management Project<br />Team: Palo Alto Research Center &amp; 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 &amp; Microsoft Cloud Service Team<br />Scope: Research on effective methods for monitoring and responding to cloud service<br />
    • 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. Observation and Analysis<br />Formative Evaluation<br />Evaluation Metrics<br />
    • 8. Example---U.S. DOT: Domain Characterization<br />
    • 9. Domain Analysis Generalization<br />Bridge the gap between high-level design concepts and fine-grain implementation of such concepts <br />
    • 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. 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
    • 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. 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 logic</li></ul>Alternative Visualization/Interaction Combinations<br />Evidence Collection and Hypothesis Generation<br />
    • 34. Summary of Designing VA for General Analysis<br />Visual<br />Analytics<br />System <br />
    • 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. 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. 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. Interaction Logging Example<br />
    • 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. Annotation Tracking and Content Sharing<br />
    • 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. Summary of Designing VA for Individual Analysis processes<br />Visual<br />Analytics<br />System <br />
    • 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. Impacts<br />
    • 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. 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. Case: Design Artifacts and Specification<br />
    • 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. Example---Xerox: Domain Characterization<br />

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