1. Supported in part by the National Science Foundation under Grant No. HRD-0734825. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Visualization Queries: University of Texas at El Paso Computer Science Trust Lab ndel2@miners.utep.edu and paulo@utep.edu www.scidesign.org Center of Excellence Sharing Resources to Advance Research and Education through Cyber-infrastructure Visualization queries specify visualization requests in terms of a universe of visualization concepts including: Views (i.e., visual perceptions of datasets) Operators (i.e., modules that transform or filter data) Parameters (i.e., values that fine tune the behavior of operators) In the presence of specifications based on these concepts, users may: be able to request for visualizations declaratively and remain unaware of the wide range of toolkit specific implementation details CI GEO ED X-INF ES We need to populate a knowledge base of visualization toolkit components from which we can query from (e.g., vtkContourFilter in Figure 3) We can model toolkit components, such as operators and formats, such using a visualization language defined using Resource Document Framework (RDF) as shown in Figure 1. The set of RDF statements describing toolkit operators will comprise our knowledge base as shown in Figure 2. Parameter Format/Type Operator hasParam buildsView hasInput/Output View Geometry isa (hasParam vtkContourFilter 35-slices) (hasInputFormat vtkContourFilter vtkImageData) (hasOutputFormat vtkContourFilter polydata) (buildsView vtkContourFilter contour) vtkImage Reader vtk Contour Filter vtkJPEG Writer dim, scalar type, byte order interval, color function {color function} {magnification} Vtk PolyData Mapper vtkImageData vtkPolyData vtkRender Window (AND(hasView ?DATA iso-surfaces) (hasContent ?DATA time.3d) (hasFormat ?DATA binaryFloatArray) (hasType ?DATA griddedTime) (viewedBy ?DATA mozilla-firefox)) Figure 1: partial visualization language Figure 2: statements modeling vtkContourFilter Figure 3: pipeline highlighting vtkContourFilter operator Visualization Query Overview Modeling Visualization Operators We define a logic based query language that is built on the predicates of the statements which compose our visualization knowledge base. Below is a template of a visualization query explaining what each variable means. Visualization Query Language Nicholas Del Rio and Paulo Pinheiro da Silva Visualization toolkits, such as Generic Mapping Tools (GMT) and Visualization Toolkit (VTK), consist of a suite of visualization operators from which users can chain together and build visualization applications. Using these toolkits can be challenging because users must: use imperative languages to write the applications, and thus understand implementation details associated with each operator, including input/output formats and parameters Background (AND(hasView -DATA ?VIEW) (hasContent -DATA +FILE) (hasFormat –DATA +FORMAT) (hasType -DATA +TYPE) (viewedBy -DATA +VIEWER)) SymbolSemantics +Bound Variable (i.e., serves as query input) -Unbound Variable (i.e., serves as query target) ?Bound/Unbound Variable requested visualization Requested view URI of dataset Format of dataset Semantic type of dataset Displays visualization Similarly to answering Structured Query Languages (SQL) queries, visualization pipelines (i.e., query plans) must be automatically derived from visualization queries. The derived pipelines, as in Figure 3, compute the query result, which in our case is a visualization. The pipelines are composed of visualization toolkit operators that are chained together. We use inference engines tailored to work with RDF (e.g., Pellet) to derive pipelines from visualization queries. Query Processing: Deriving Pipelines The example query below is requesting an iso-surfaces rendering of a gridded time field (Figure 4) associated with a seismic tomographic velocity model. Figure 5 presents an overall view of how all the components (i.e., visualization query, knowledge base, and pipelines) are related. Example: Hole’s Code Gridded Time Field Figure 5: High level view of the interrelationships between queries, knowledge bases, and pipelines RDF inference engine (e.g., Pellet) A Declarative Approach To Generating Visualizations Using the Semantic Web Figure 4: gridded time field visualized as iso-surfaces query for gridded time field visualization