Scientific Data Management
Workshop Series, Part III
Report of the
Integrated Data Analysis and Visualization
Breakout Group
May 25, 2004
Outline
• Where are we now?
• Where do we want to be?
• Sound bite vignettes and a shameless
•
•
plug.
Recommendations.
Nagiza’s slides.
Where Are We Now?
• Single-point examples of integrated data
analysis and visualization environments in
certain communities.
• “root” in HENP, Jim Gray’s SkyServer portal for the Sloan
Digital Sky Survey.
• “Capability wall”
• Familiar tools do not scale to present and imminent
requirements.
• New capabilities needed to aid in data understanding (e.g.,
query-based data analysis and visualization).
Where are We Now? (ctd.)
• Communities go through “scatter-gather”
phases.
• Scatter means divergence, gather means convergence.
• Scatter happens when individual PI’s and their code are in a
hurry.
• Gather happens when there needs to be some form of crossproject interaction, like code/data validation or data sharing.
• Newer fields (endeavors) tend to have data
standards.
• E.g., GenBank genome sequence data format.
Where Do We Want to Be?
• Unrestricted exchange of data, software tools, workflows:
• Within communities, and
• Between communities when feasible and practical.
• Community-based data model standards.
• Easier for some, more difficult for others.
• Scientists want the ability to use an integrated data
analysis and visualization pipeline w/o the need for an
expert.
• Should protect existing investment in known procedures.
• Integrated data analysis and visualization workflows that
support:
• Diverse and distributed resources.
Sound Bite
• “The real power came when we agreed upon a
standard data API; it is hopeless to standardize
on a common visualization tool.”
• A common data model provided stability in “the
foundation” and promoted “freedom of choice”
in data analysis and visualization.
• Flexibility and adaptability are the hallmarks of
useful systems (e.g, the “duct tape test.”)
Sound Bite(s)
• “There is no force to motivate common output
formats.”
• “People won’t consciously decide to share
data.”
• Common data format/model for chemistry input
to many combustion codes (a necessity), but the
same codes output data in different formats.
• Output format is chosen as a function of
expediency, custom needs, etc.
Sound Bite
• “3D visualization is good for navigating through
complex datasets to set up 2D quantitative data
analysis.”
• The human cognitive processing system is adept
•
at multiscale information processing.
This example shows use of “best of breed”
technology (or, in other words, “the right tool for
the right job.”)
Shameless Plug
• The “Visualization Center” idea is strongly endorsed in a
limited sampling of applications.
• The Visualization Center consists of:
• Core visualization technology focus,
• Matrixed staff from applications, data management, networking, etc.
• The Product is:
• Integrated visualization and data analysis technologies focused on
specific application areas.
• Community-based data models, requirements are highly
desirable to maximize impact.
Recommendations
• A “carrot” or “stick” will be required to
lure/coerce communities towards standards.
• Is funding a “carrot” or a “stick?”
• Promote efforts to unify communities around
•
data models, which form the basis for integrated
data analysis and visualization resources.
Promote efforts to build community-based
integrated data analysis and visualization
software around these data models.
Part Two….
Nagiza’s slides.