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Overview of "Define Visualization Processing Pipeline" Use Case

Define a processing pipeline for one visualization technology.


Tip: Key Points
UC Priority= 4 or 5: Critical, is in R2
Only boldface steps are required
<#> before a step —> lower priority
(optional) —> run-time option

Related Jira Issues:   Open   •   All

Metadata

Refer to the Product Description and Product Description Release 2 pages for metadata definitions.

Actors Data Process Programmer
References  
Uses UC.R2.51 Define Execution Engine
UC.R2.47 Define Executable Process
UC.R2.21 Transform Data in Workflow
Is Used By UC.R2.18 Visualize Data Product
UC.R2.19 Produce Matlab Visualization
Extends  
Is Extended By  
In Acceptance Scenarios None
Technical Notes Developer uses the visualization processing framework to add a new processing pipeline.
Lead Team AS
Primary Service Data Analysis & Visualization Services Part 1
Version 2.0.2
UC Priority 4
UC Status Mapped + Ready
UX Exposure PRG

Summary

Define a processing pipeline for generating real-time visualizations using a specific visualization technology. (A processing pipeline is an electronic description of subsequent steps and intermediate formats such that visualization data products can be generated in real-time.) Visualization methods may include Matlab-compatible visualizations, scene rendering, Google Maps visualization. Pre-defined visualization process algorithms are executed using such visualization processing pipelines on pre-defined Execution Engines.

Assumptions

  • There are three parts to the visualization generation: the processing pipeline definition, the engine that executes the visualization and the script that produces a visualization
  • All necessary Execution Engines for data transformations and visualization generation exist (see UC.R2.51 Define Execution Engine)
  • All Data Process Definitions required for data transformation and visualization generation exist (see UC.R2.47 Define Executable Process)
  • In R2 the processing pipeline is defined by chaining DataProducer resources; in R3, the AS workflow capabilities will be used to define the processing pipeline (e.g. using Kepler).
  • Visualization outputs are produced within the Integrated Observatory, and are streamed as a normal data product.
  • These steps are performed by a programmer developing software, and so do not require a user interface.

Initial State

Data Process Developer is ready to enter visualization method definition.

Scenario for "Define Visualization Processing Pipeline" Use Case

  1. Data Process Developer defines visualization processing pipeline
    1. Define Data Transform for each processing step in the processing pipeline
      1. Define constraints on input data structure/type for transform
      2. Select existing Data Process Definition for predefined processing step (e.g. data transformation, visualization script)
      3. This references the Execution Engine applicable for the processing step (initial focus on Matlab, Python and Python controlling a C/Fortran program Execution Engines)
      4. Define output data format
  2. Data Process Developer defines visualization pipeline parameters
    1. Define parameters by name and type that users can select from or fill to customize visualizations
  3. Data Process Developer defines visualization pipeline metadata
    1. Define default notification events to users
    2. Define scheduling constraints, e.g. limits on triggering event/frequency
    3. Any data that can be automatically supplied by the system at execution time (e.g., execution time, execution platform, author) will be.
  4. Data Process Developer sets the processing pipeline life cycle state to active
    1. This will make the pipeline available for selection and scheduling (not initiate its execution).

Final State

The visualization processing pipeline definition is registered and can be selected to schedule a specific visualization product generation according to the prescribed schedule.

Comments

These comments provide additional context (usually quite technical) for editors of the use case.

Requires an extensible framework for the integration of data visualization tools (specification of standard formats, intermediate processing steps and a configurable pipeline (workflow) of generating a visualization from a dataset) and workflow framework for visualization workflows (supports developer-created workflows for the visualization of data products based on measurement data, using graphics data to visualization applications).

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