Published on 11-Jul-2013
"It’s not just the fact that you can do this 100 times faster than before. It's that you can begin to treat your data with a fair degree of discrimination and refine data concerning multiple phases in a way that was not previously possible." - Dr. N. Stewart McIntyre, Professor Emeritus, Department of Chemistry, University of Western Ontario
University of Western Ontario
Big Data, Smarter Planet
University of Western Ontario scientists have launched groundbreaking work that enables researchers to remotely run experiments and analyze data in near-real time from synchrotrons—large particle accelerators that help scientists explore how different materials behave under specific conditions. This allows researchers, for the first time, to analyze results and change parameters mid-experiment, which often leads to greater discoveries. The benefits will be far reaching—from improving the reliability of jet engines to helping mining companies increase precious metal yields.
University of Western Ontario wanted to accelerate the scheduling and conducting of scientific experiments by using more data on a particle accelerator, the most sophisticated tool used in physics research.
The university built a remote experiment and processing solution to help the research community collaborate and carry out experiments without having to travel to a facility. To conduct their work, researchers can now remotely set parameters, run tests and immediately process results from multiple data streams. Faster results and more data streams allow researchers to consider alternative theories, test them and share them with other researchers to solve the big challenges of industry, energy, healthcare and bioscience fields.
Reduces data processing time drastically from weeks to minutes—100 times faster than previously Decreases, and in some cases, eliminates, travel costs associated with conducting experiments Advances innovation by enabling researchers to more easily and quickly test new theories Supports increased collaboration
Helping scientists conduct more in-depth experiments faster.
Scientists at the University of Western Ontario have launched groundbreaking work that enables researchers to remotely run experiments and analyze data in near-real time from synchrotrons—extremely large particle accelerators that help scientists explore how different materials behave under specific conditions. This allows researchers, for the first time, to analyze results and change parameters mid-experiment, which often leads to greater discoveries. The benefits will be far reaching—from improving the reliability of jet engines and nuclear reactor components to helping mining companies increase precious metal yields.
|Smarter Research:||Enabling scientists to test more theories, more quickly|
|Instrumented||Diffraction, spectroscopic and imaging data are processed and sent from remote beamlines at the synchrotron to research labs through a web interface.|
|Interconnected||Experimental results are displayed on the researchers’ screens via a web interface and can be accessed by researchers in various locations for collaborative or individual experimentation.|
|Intelligent||Researchers can refine parameters mid-experiment based on near-real-time data to gain more accurate and precise research results.|
Many scientists use a machine called a synchrotron to observe how different materials behave under specific conditions. About the size of a football field, these machines produce X-ray beams that help scientists obtain critical information about materials, such as their atomic and crystalline structures. This research is critical to solving big industry challenges, such as helping aerospace companies build stronger and lighter components for planes.
However, because of the complexity and expense of construction, there are only 50 synchrotrons worldwide, and researchers must schedule time and travel to conduct their experiments. Once the experiments are completed, it can take researchers weeks to process and analyze the data.
For Dr. N. Stewart McIntyre, who has been involved in physical and analytical chemistry research for nearly 50 years, the question was: How can we remove these barriers to foster greater scientific discovery?
Dr. McIntyre, who is professor emeritus of chemistry at the University of Western Ontario, contacted Dr. Michael Bauer, a professor with the University of Western Ontario’s Computer Science Department, and representatives from IBM to discuss the problem.
Together, they wrote to Canada’s Advanced Research and Innovation Network (CANARIE) for funding. CANARIE is a non-profit corporation that provides grants for digital infrastructure projects in Canada.
“Two needs were paramount,” says Dr. McIntyre. “First, we wanted to provide ready access to the beamline using remote web-based technologies. Secondly, we wanted to enable the rapid processing and analysis of the massive data sets.”
Providing researchers remote access to synchrotrons
With funding in place, Dr. McIntyre and Dr. Bauer began working with IBM® Global Business Services® to create a framework that would reduce or eliminate the need for scientists to travel to the synchrotron.
Called Science Studio, the framework uses a Service Oriented Architecture (SOA) system environment that provides scientists with remote access to the synchrotron through a web-based interface. Using this portal, scientists can operate, observe and record essential parts of their experiments from their offices.
The first implementation of Science Studio provides researchers with remote access to the VESPERS (Very Sensitive Elemental and Structural Probe Employing Radiation from a Synchrotron) beamline—one of several X-ray beams at the Canadian Light Source, Canada’s national center for synchrotron research.
Tackling big data
To tackle its big data challenges, Dr. McIntyre and Dr. Bauer worked with IBM Research to develop an extension to the Science Studio platform called ANISE (Active Network Interchange for Scientific Experimentation). This network uses IBM InfoSphere® Streams software to rapidly analyze thousands of images with real-time analytic processing, filtering background noise to provide researchers with the relevant data.
Typically, researchers capture about 20,000 images—or about 50 gigabytes of data—all within the space of two to three hours. A two-day experiment using a synchrotron previously took weeks to process. Now, using InfoSphere Streams software, researchers can review experimental outputs in minutes and test alternative theories as their experiment is underway. Data from previous experiments can also be uploaded to the network and analyzed using InfoSphere Streams, fostering greater collaboration among researchers.
“One of the advantages of InfoSphere Streams is flexibility,” says Dr. Bauer. “As we adopt different processing strategies and algorithms, it is very straightforward to modify and accommodate these changes. When an experiment is running, researchers can stream the data as it is collected and receive the results in minutes.”
IBM WebSphere® MQ software collects the data, prioritizes requests (for example, near-real-time streams are processed ahead of batch requests) and sends the data to InfoSphere Streams for processing. The entire solution runs on a cluster of ten IBM BladeCenter® servers.
ANISE is currently available to researchers conducting experiments on the VESPERS beamline at the Canadian Light Source and at Advanced Light Source, another synchrotron facility located in Berkeley, California.
Fostering scientific discovery
This ability to remotely run experiments and rapidly analyze data from synchrotrons is leading to exciting discoveries in the fields of metallurgy and mineralogy.
“It’s not just the fact that you can do this 100 times faster than before,” Dr. McIntyre says. “It's that you can begin to treat your data with a fair degree of discrimination and refine data concerning multiple phases in a way that was not previously possible.”
For example, recent research on a metal alloy commonly used in jet engines and nuclear reactors revealed that an oxidized outer layer that protects the metal from corrosion actually can cause the metal to crack.1
“We have been able to see the accumulation of strains over a period of time that actually led to the initiation of a crack—a first for the science,” says Dr. McIntyre.
Other research using this technology will help engineers and scientists precisely map mineral structures—a huge advance that will enable mining companies to increase yields while reducing costs.2
Dr. McIntyre explains: “A classic example is arsenopyrite and various diffraction pattern maps of arsenopyrite that may contain gold. This technology gives engineers and scientists the ability to make a mineral by mineral assessment. That's new. That's come as a result of this immense computational power and it has enormous application in helping gold mining companies disseminate their ore bodies.”
- Reduces data processing time drastically from weeks to minutes—100 times faster than previously
- Decreases, and in some cases, eliminates, travel costs associated with conducting experiments
- Advances innovation by enabling researchers to more easily and quickly test new theories
- Supports increased collaboration
The inside story: Getting there
According to Dr. Bauer, one of the key questions any organization should ask as they approach big data is: What data do you need to store? For example, in this project, researchers may save their own data; but the ANISE service does not.
“The real question is: Can you extract value from the data in such a way that you're only keeping the things that are valuable rather than trying to collect everything,” says Dr. Bauer.
For more information
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To get involved in the conversation, visit: www.ibmbigdatahub.com
For more information about the University of Western Ontario, visit: www.uwo.edu
For more information about Science Studio and ANISE, consult the following publication:
“Remote Internet Access to Advanced Analytical Facilities: A New Approach with Web-Based Services.” N. Sherry, J. Qin, M. Suominen Fuller, Y. Xie, O. Mola, M. Bauer, N. S. McIntyre, D. Maxwell, D. Liu, E. Matias, C. Armstrong, Analytical Chemistry, 2012, 84 (17), 7283–729.
Products and services used
© Copyright IBM Corporation 2013 IBM Corporation Software Group Route 100 Somers, NY 10589 Produced in the United States of America July 2013 IBM, the IBM logo, ibm.com, BladeCenter, Global Business Services, InfoSphere, and WebSphere are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at ibm.com/legal/copytrade.shtml This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. It is the user’s responsibility to evaluate and verify the operation of any other products or programs with IBM products and programs. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. Actual available storage capacity may be reported for both uncompressed and compressed data and will vary and may be less than stated. 1 “Plastic and elastic strains in short and long cracks in Alloy 600 studied by polychromatic X-ray microdiﬀraction and electron backscatter diffraction.” Jing Chao, Marina L. Suominen Fuller, Nathaniel Sherry, Jinhui Qin, N. Stewart McIntyre, Jaganathan Ulaganathan, Anatolie G. Carcea, Roger C. Newman, Martin Kunz, Nobumichi Tamura, Acta Materialia, http://dx.doi.org/10.1016/j.actamat.2012.06.060 (2012). 2 “Acquisition, Sharing, and Processing of Large Data Sets for Strain Imaging: An Example of an Indented Ni3AI/Mo Composite.” N. S. McIntyre, R.I. Barabash, J. Qin, M. Kunz, N. Tamura, and H. Bei, JOM, http://jomgateway.net/ArticlePage.aspx?DOI=10.1007/s11837-012-0496-9 (2012).