Capturing and Using Knowledge about Visualization Toolkits


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Presented at AAAI Fall Symposium on Discovery Infomatics

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Capturing and Using Knowledge about Visualization Toolkits

  1. 1. Capturing and Using Knowledge about Visualization Toolkits Nicholas Del Rio Paulo Pinheiro - PNNL 1
  2. 2. Outline• Discovery through Visualization Diversity• Background on Generating Visualizations• Visualization Conceptual Model• Visualization Knowledge Base• A Practical Application• Conclusion 2
  3. 3. Service Discovery for VisualizationThere are many ways tovisualize a single dataset Near neighbor vs. surface 3D views: gridding techniques isosurfaces vs. point plot In many cases, it is up to the users to understand the different views and know how to generate them 3
  4. 4. Service Discovery for VisualizationThere are many ways tovisualize a single dataset Near neighbor vs. surface 3D views: gridding techniques isosurfaces vs. point plot How can we support the seamless automated discovery of visualization services to support visualization diversity? 4
  5. 5. GoalEnable automated discovery and integration ofvisualization services VISUALIZE AS isosurfaces IN firefoxObjectives: WHERE AND FORMAT TYPE = csv = gravity AND interval =5 AND xRotation = 10 Abstract visualization pipelines in 1 the form of declarative requests (visualization queries) Construct a knowledge base of 2 visualization services Develop methods for translating 3 the abstractions into pipelines (query answering) 5
  6. 6. Proposed Usage PatternUsers may also generate other visualizations of the same dataset from a variety of sources 6
  7. 7. BackgroundUsers typically employ visualizationtoolkits to construct visualizationsSequence of visualizationoperators known as apipeline 7
  8. 8. Visualization Pipeline StructureOp1: vtkDataObjectToDataSetFilter Op Data Gathering 1 1 OpOp2: vtkShepardMethod 2 Mapping 2 OpOp 3: vtkExtractVOI 3Op 4: vtkContourFilter Op Visualization Abstraction 3 4 specified in the query OpOp 5: vtkPolyDataMapper 5 Rendering 4Data Flow Model – Haber and McNabb 90 1 2 4 Data State Model – Chi 98 2 3 8
  9. 9. Building From Existing WorkHaber’s work paved the way for modular visualizationenvironments popular in the 90’s: – Visualization Data Explorer (OpenDX) and IRIS – A Visualization System (AVS) and Visualization Toolkit (VTK) – Users still have to manually compose pipelinesChi’s work provided a data centric perspective fromwhich to compare and taxonomize techniquesThese models have not been used to drive automaticcomposition of visualization pipelines 9
  10. 10. Building From Existing Work (2)Past efforts to automate visualization generation havehad great success in restricted domains:• A Presentation Tool (APT) – Jock Mackinlay 86• Tableau – Stolte 2012Both operate on relational data to drive visualizations:• Nominal or ordinal These tools were not designed to operate on• Functionally dependent general kinds of data and are were more focused towards information visualization 10
  11. 11. Our Enhancements: Format [Type] One way we expand on existing visualization models is by considering type and format requirements of modules. We call these modules transformers CSV [Gravity] OBSERVATION 1 Op 1 Format is not enough, some can XML [vtkPolyData] encode a variety of types Op 2 OBSERVATION 2 XML [vtkImageData3D] Dimension reduction is not explicitly Op 3 specified but inferred through the type requirements XML [vtkImageData2D] Op 4 OBSERVATION 3 XML [vtkPolyData] These formats and types should be defined in ontologies and shared to Op 5 foster interoperability JPEG [owl:Thing] 11
  12. 12. Our Enhancements: ViewerWe also consider the viewer that presents the visualizationOp 1Op After the mapping, there may be a number of transformations 2 before the geometry can be presented by a viewerOp 3 These additional transformations may JPEG [owl:Thing] PDF [owl:Thing] be viewed as an expansion of theOp rendering phase 4Op Op 5 6 PDF Viewer (Type Agnostic) 12
  13. 13. Our Overall Model1. Marries Data Flow (Haber) with Data State (Chi) concept of Visualization Abstraction2. Incorporates service composition concerns (i.e., format [type]) into data gathering phase3. Incorporates concept of a Viewer4. Expands rendering phase to consist of a sub pipeline of further transformations5. Is encoded in OWL 13
  14. 14. Constructing the Knowledge BaseWe can classify visualization services using the concepts in our ontology:• Transformer• Mapper (generates visualization abstractions)• ViewerServices are combined based on our model constraints:• Format[type] match-ups• Must include a mapper• Must terminate at a viewer Transformer Mapper Viewer 14
  15. 15. A Data Centric View 15
  16. 16. Answering Visualization QueriesVISUALIZE http://somedata.csv Visualization Queries Specify:AS 3d-point-plot IN firefox • Source format[type] WHERE FORMAT = csv AND • Target Visualization Abstraction TYPE = gravity-data • Target Viewer Transformer Mapper Viewer 16
  17. 17. Sharing Visualizations Recipient may be unable to adjust any properties such as contour interval, color tables, projection and labels 1. Send image (contents or by URL) Recipient may not have tools, capabilities, and expertise to regenerate visualization from data 2. Send data These solutions have been implemented only for specific domains , for example OGC3. Send URL of visualization embedded in viewer VisKo queries address the limitations above4. Send a VisKo Query specifying the visualization 17
  18. 18. Conclusion• Visualization queries abstract away the complexities of visualization pipelines.• We can automate pipeline construction provided: – A visualization query – A service knowledge base structured using our model• We can use queries to share visualizations in a way that empowers visualization recipients. 18
  19. 19. Future Work• Automated Parameter Settings – Color functions driven from formula identification – Data driven vs. visualization driven• Weighted graphs – Add information about performance – Add information about quality degradation• Task driven generation – Map task descriptions (Shneiderman 96) to the right set of parameters and visualization abstractions 19
  20. 20. Play With Our System! VisKo Server 20