A growing number of scientific fields suffer from a stifling
embarrassment of riches: data pile up much faster than they
can be analyzed. A team of researchers at the University of
Southern California Viterbi School of Engineering's
Information Sciences Institute is now
building a prototype of a system that will address the
problem by automating scientific workflows.
ISI computer scientist Yolanda Gil leads the newly funded
$13.8 million Windward project, aiming at (in its full title)
"Scaleable Knowledge Discovery through Grid
Workflows."
Gil (below left) says that in fields like climatology,
high energy physics and seismic modeling "our ability to
gather data is surpassing our ability to analyze it. Our data
warehouses are becoming data graveyards." "
In a sense, Windward will bring to the analysis of scientific
problems an approach similar to that of industrial
engineering, where engineers create optimal workflows to
bring together raw material and machinery in the most
efficient fashion to create product.
he product in modern science is not
a physical item like an automobile or computer, but rather a
model, or an understanding. But efficient workflows to create
it are equally critical - and, because the raw material is
information, not matter, much more automatable. Gil
and ISI collaborator Ewa Deelman co-chaired an NSF
workshop on the subject in May 2006.
"Significant scientific advances today are achieved through
complex distributed scientific computations," their overview
for this workshop noted. "These computations, often
represented as workflows of executable jobs and their
associated dataflow may be composed of thousands of steps
that integrate diverse models and data sources."
The workshop held out the possibility of computer science
being able to channel this waterfall of data into orchestrated
workflows, leading to recommendations for "basic work in
computer science to create a science of workflows," and
suggested that scientists proactively build workflow
architecture into their research plans: "workflow
representations that capture scientific analysis at all levels
should become the norm when complex distributed scientific
computations are carried out."
Windward is an effort by Gil, who is principal Investigator
and Project Leader of the ISI Interactive Knowledge Capture
research group, Deelman, and two fellow ISI project
leaders, Paul Cohen and Carl Kesselman.
They believe they can accomplish this ambitious task by
integrating two longtime ISI specialties, artificial intelligence
and grid computing.
AI tries to give computers power to respond accurately and
appropriately to changing and novel circumstances, bringing
multiple concerns to bear on the problem of making the right
choice from a number of alternatives. Cohen
(right)
will build on his work at the ISI Center for Research on
Unexpected Events (CRUE), which has focused on AI
systems for complex data analysis. Cohen has been working
specifically in the area of AI analysis of scientific data for
years, publishing papers on "Intelligent Assistance for
Computational Scientists: Integrated Modeling,
Experimentation, and Analysis" ten years ago with work on
planning systems going even farther back.
He has also studied the history of science in certain field to
try to see patterns in the process of discovery, work that
underlies the approach.
In order for AI systems to automate processes and provide
assistance to scientists in defining workflows of complex
computations, they need to have the world carefully
structured and described. Gil has long been active in
developing the semantic web, which creates a digital
universe that AI can explore and understand, and which will
be a building block of the Windward system.
Finally, previous AI systems have been much, much smaller
than the regional, national and even intercontinental data
structures needed to do workflow science. This is where
grid computing and Deelman and Kesselman come in. Since
1996, Kesselman has been perfecting the Globus software
that allows multiple users in multiple locations secure and
easy and transparent access not just to raw data, but also to
resources (computers) to process the data.
Linking to grid computing software, Deelman and her
collaborators
have developed a workflow management system, called
Pegasus, that maps large numbers
of computations to distributed resources while optimizing the
overall performance of the application.
Deelman will continue to evolve Pegasus, which has already
been successfully used in applications in the fields of
astronomy, earthquake science, gravitational-wave physics,
and others.
The AI and grid computing groups at ISI have been
collaborating in the area of scientific workflows for several
years now, with notable results in earthquake science in joint
work with the Southern California Earthquake Center.
In the Windward project, they will develop new workflow
techniques to represent complex algorithms and their subtle
differences so that they can be automatically selected and
configured to satisfy the stated application requirements.
They will also investigate mechanisms to support
autonomous and robust execution of concurrent workflows
over continuously changing data. In addition, they will
develop learning techniques to improve the performance of
the workflow system by exploiting an episodic memory of
prior workflow executions.
Gil with David DeRoure and Jim Hendler co-edited a January
2004 special issue of IEEE
Intelligent Systems journal on "E-
Science," putting forth many of the
ideas Windward will develop.
Funding for the project
comes from the Air Force Research Laboratory.
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