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November 03, 2006
The Yin and Yang of Understanding Data
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When the Department of Energy's Office of Science announced the latest round of awards in the Scientific Discovery through Advanced Computing (SciDAC) program in September, the funded projects included a new Center for Enabling Technologies that will focus on meeting the visualization and analytics needs of scientists. Called the SciDAC Visualization and Analytics Center for Enabling Technologies, or VACET, the project will be co-led by Wes Bethel, head of the Visualization Group at Lawrence Berkeley National Laboratory, and Chris Johnson, director of the Scientific Computing Institute of the University of Utah. The VACET team also includes researchers at Lawrence Livermore and Oak Ridge national laboratories, the University of Utah and the University of California at Davis.
At the upcoming SC06 conference in Tampa, Bethel will give an overview and discuss the objectives of VACET in a talk at the LBNL booth (1812) at 11 a.m. Wednesday, Nov. 15. In the runup to SC06, Bethel took some time to talk about the field of analytics, how it contributes to computational science and where the fields of analytics and visualization are headed.
Question: First of all, how did the team for your SciDAC project come together?
Bethel: We wanted to strike a balance where we have members with research and production backgrounds. The result is a team with members from well-known university and DOE laboratory research programs along with representation from DOE's production visualization programs. This kind of blend will assure that we have top-quality research and development as well as the means to deploy the new technology at the computational facilities where the data will be computed, collected, stored and made available to distributed teams of SciDAC science researchers.
Chris Johnson from the University of Utah and myself share leadership duties of this project. Our team consists of Valerio Pascucci, Hank Childs and Peer-Timo Bremer from LLNL; Ken Joy and Bernd Hamann from UC Davis; Chris Johnson, Chuck Hansen, Claudio Silva, Steve Parker, Allen Sanderson, Xavier Tricoche and Marty Cole from the University of Utah; Sean Ahern, George Ostrouchov, and Jeremy Meredith from Oak Ridge National Laboratory; and of course Cristina Siegerist and myself from Berkeley Lab.
Every person on this team is "a superstar." In four of the past eight years, members of this team have authored or co-authored Best Paper Award-winning papers at the IEEE Visualization conference. All have an impeccable record of publications and are recognized leaders in the field. But not only that, this team has architected and developed a vast amount of visualization software that has had a broad impact in DOE and NSF science and research programs. Not only are they first-class professionals, they are also fun to hang out with. One thing that is special about this team is that we all get along great. This combination really makes this team special to me. I'm honored to be working with such a great bunch of people.
Q: Last year, SC05 incorporated analytics into the conference program and this year, DOE agreed to fund your proposal to establish a center for analytics and visualization. Clearly, it's an emerging field. What's your definition of analytics and how does it fit into HPC?
Bethel: That's a question that reminds me of freshman philosophy class and the question, "What is love?" You can give a number of examples, but it's hard to define.
We can start by defining scientific visualization, which is the process of creating images from abstract scientific data. Analytics has been described as the science of analytical reasoning. To me, analytical reasoning means being able to draw conclusions based on hypothesis testing and the exploration of large, complex and occasionally incomplete data. Visual analytics is a way of facilitating such research through the use of interactive visual interfaces. Visualization and analytics are complementary technologies with no clearly defined boundary between them. Visualization helps accelerate analytics by relying on humans' vast cognitive processing abilities, the "yin" side of data understanding. Analytics gives hard, quantifiable measures, and is the "yang" side of data understanding.
An example of analytics could be how the California Department of Forestry responds when a forest fire is reported. Their incident response is based on figuring out what's wrong, what they need to do and what resources they have to do that. In simulation science, lives and property may not be at stake, but there can be substantially more data involved. For example, policy makers want to understand how greenhouse gas emissions at various levels will impact global climate. Climate scientists study this question by performing lots of simulation runs where they vary the input parameters to the simulation. Visualization and analytics are tools for discovering relationships between cause and effect in a highly complex system that produces many terabytes worth of data. Neither of these is really a complete "definition of analytics," but both are examples of how analytics is put into practice to solve complex problems.
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