Download Research Tools
When wildfires strike, all eyes turn to the clouds, hoping for a downpour that will quench the flames. Now, wildfire prevention teams on the Greek island of Lesvos are looking to a different kind of cloud for help, thanks to the VENUS-C Fire application and the computing power of Windows Azure.
The Fire app determines the daily wildfire risk on Lesvos during the months of May to October, when the annual dry season turns the island’s forests into a tinder box. The application not only alerts fire prevention teams of the risk, it also enables firefighters to design and coordinate an effective response when a wildfire breaks out. As a result, the island’s fire prevention personnel have been better prepared to predict, respond to, and stop fires, preventing potential loss of life and property.
The Fire app integrates Bing Maps, Microsoft Silverlight, and Windows Azure in a single system that enables users to see the potential of an emerging fire
Developed by the Geography of Natural Disasters Laboratory at the University of Aegean in Greece, the Fire app is designed to calculate and visualize the risk of wildfire ignition and to simulate fire propagation. The end users are primarily emergency responders, including the fire service, fire departments, and civil protection agencies that address wildfires on the island of Lesvos.
The app was built with functionality from multiple resources, giving it both technological depth and a visual interface that is accessible to non-technical users. It integrates Bing Maps, Microsoft Silverlight, and Windows Azure in a single system that enables users to see the potential of an emerging fire.
All of the Fire app’s data is stored in the cloud via Windows Azure. And a lot of data it is, including information on topography, vegetation, weather patterns, and past fire patterns. This is “big data,” and crunching it requires the computing power of a large cloud infrastructure, such as Windows Azure.
Professor Kostas Kalabokidis of University of the Aegean calls Windows Azure essential to the app, noting that “the cloud provides us with the necessary processing power and storage that is required. That means the real end users for the fire department do not need to have any huge processing power or storage capabilities locally.” Indeed, on the end-user side, all that’s needed to access the tool is a regular computer or laptop, an Internet connection, and a web browser that supports Silverlight.
The Geography of Natural Disasters Laboratory team built the Fire app in 2011. Microsoft Research partnered with the lab during the development phase, providing funding, high-performance computing resources, and cloud computing infrastructure. As part of that collaboration, Microsoft built a tool called the Generic Worker (GW) that greatly simplified the challenges faced by Kalabokidis’ team.
GW was critical, according to Professor Kalabokidis, who states that “Generic Worker provides a robust environment for job execution that fulfilled the requirements of the University of the Aegean’s scenario for running forest fire risk and fire propagation models in the cloud. GW provides interoperability through OGF [Open Grid Forum] Basic Execution Service, which is very important in the Aegean scenario to execute tasks in a hybrid cloud environment, such as VMs [virtual machines] of different cloud solutions. Furthermore, GW provides scalability: for example, VMs are increased or decreased according to the needs of deployment. Users are also notified about the status of the job, which is important for the execution of the fire propagation simulation.”
The Fire app is just one of many big data projects that benefit from Windows Azure’s scalability, storage capacity, and computational power. There’s no question but that cloud computing is having a significant impact throughout the research world, as information from instruments, online sources, and social media are combining to create a data tsunami. This has ushered in the era of data-intensive science—what the late Jim Gray predicted would be the Fourth Paradigm of scientific research—and Windows Azure is in the forefront of making it possible.
Cloud computing, and the processing power that accompanies it, has made it possible for researchers to reduce processing job times from months to just hours. The thing that excites me about my job is the possibility that we can change the way science is conducted. I believe that cloud computing is a revolutionary change in an era of big data and the exploration of large data collections.
—Dennis Gannon, Director of Cloud Research Strategy, Microsoft Research Connections
Learn More
Over the past two years, I have watched eScience take root in China. The movement advanced in the first and second Chinese eScience forums and in various eScience projects that were developed by the Computer Network Information Center (CNIC) of the Chinese Academy of Sciences (CAS). During this time, Microsoft Research collaborated closely with the CAS, exchanging ideas through joint workshops, student contests, and lectures such as the keynote that Tony Hey, vice president of Microsoft Research Connections, delivered at the CAS meetings in 2010.
Through these channels, a foundational concept of eScience—that we are entering a new fourth paradigm for science where discovery advances through data-intensive computing—was introduced to the Chinese eScience community and attracted the attention of the CAS. In late 2010, Xiaolin Zhang, the executive director of the National Science Library of the CAS, proposed a Chinese translation of The Fourth Paradigm, a seminal collection of essays that describe the practice and promise of data-intensive science. I am happy to report that through the efforts of the CAS and the support of Microsoft Research, the Chinese edition of The Fourth Paradigm premiered in Beijing on October 23.
Tony Hey and Stewart Tansley, two of the book’s co-editors, joined Lolan Song, Steve Yamashiro, and me at the launch event. On behalf of Microsoft Research, Tony donated copies of the book to more than 80 Chinese university libraries, observing that "The advance of science depends on how well researchers collaborate with one another, and marry science with technology." I, for one, am confident that the publication of the Chinese edition of The Fourth Paradigm will foster just such endeavors.
Jiaofeng Pan, the deputy secretary-general of the CAS and one of the book’s Chinese translators, spoke highly of the Chinese edition. “Building on the studies from the field of eScience, the book proposes the fourth paradigm for scientific research: data-intensive science as well as academic exchange based on big data. This book opens the door to a new paradigm of scientific research, greatly enhancing awareness of the huge impact of the digital revolution in the research and information network.”
Through the release of the Chinese edition, we sincerely hope to help Chinese researchers in a variety of fields to understand and utilize this revolutionary development in research methodology. To further speed the adoption of data-intensive approaches to research, Microsoft Research has agreed to donate 2 million hours of access to Windows Azure cloud resources, as well as 15 terabytes of Windows Azure storage space, to research projects at the CNIC over the next two years, which will enable Chinese researchers to apply the concepts of the fourth paradigm by using the Windows Azure platform.
In 2013, the IEEE International Conference on e-Science and the Microsoft eScience Workshop will be held jointly in Beijing. Looking forward to those events, we anticipate even more progress in eScience research in China.
—Guobin Wu, Research Program Manager, Microsoft Research Asia
Can scientists predict what happens when they introduce a change into a living system—for example, if they change the structure of a gene or administer a drug? Just as changing one letter can completely change the meaning of a word, the change of a single letter of the genetic code (referred to as a single nucleotide polymorphism, or SNP) can subtly affect the meaning of a gene’s instructions or alter them completely, making the effect of any change extremely hard to predict. Such changes are thought to be responsible for much of the variation between members of a single species—for example, in susceptibility to different diseases. The ability to successfully predict the effect of such changes would accelerate drug discovery and provide a deeper understanding of the processes of life.
In collaboration with Jasmin Fisher at Microsoft Research Cambridge, professor Yanay Ofran and his colleagues at Bar Ilan University have embarked on a program of scientific research that aims to resolve some of the questions underlying this overall goal, and some of their early results have now been published.
One of the researchers’ first tasks was to determine whether it is possible to predict how a complex network of biochemical interactions will change when a SNP (pronounced “snip”) alters the function of one of the network’s components. In an August 2012 paper entitled, “Static Network Structure Can Be Used to Model the Phenotypic Effects of Perturbations in Regulatory Networks” (available at Bioinformatics with paid subscription), the authors describe their success in analyzing static models of biological networks and correctly predicting the response to changes more than 80 percent of the time. This enables the functions of the network to be deduced, the foundation for building a more expressive dynamic model.
Building static networks is a challenge in itself; before beginning this work, the researchers needed to understand which genes are active in a particular cell and what they do. In their latest publication entitled, “Assessing the Relationship between Conservation of Function and Conservation of Sequence Using Photosynthetic Proteins” (available at Bioinformatics with paid subscription), the Ofran lab has shown that, while sets of related genes with similar structure diverge in function more quickly than previously thought, selected smaller pieces of each gene may still be useful in predicting function.
There are many unresolved challenges along the way to the eventual goal of predicting the effect of a SNP—understanding which genes are switched on in which cells and how drugs interact with proteins are just two active areas of investigation—but once the goal is reached, an understanding of the functions of all genes and how changes affect biological systems could lead to the development of computational models to predict and cure many diseases.
—Simon Mercer, Director of Health and Wellbeing, Microsoft Research Connections