This article/post is from a third party website. The views expressed are that of the author.We atCapacity Building & Developmentmay not necessarily subscribe to it completely. The relevance & applicability of the content is limited to certain geographic zones.It is not universal.
TO VIEW MORE CONTENT ON THIS SUBJECT AND OTHER TOPICS, Please visitKNOWLEDGE-KORRIDOR our Virtual Library
Tuesday, October 14, 2014
Understanding how the Cloud can drive Data Research 10-15
Understanding how the Cloud can drive Data Research
Science and Information Conference recently invited Kenji Takeda, Solutions Architect and Technical Manager for the Microsoft Research, to deliver an industrial workshop during the conference as part of their ongoing relationship with Microsoft. Dr. Takeda spoke about Data-driven discovery and the cloud, mainly Azure4Reasearch which is a Microsoft product.
Dr. Takeda started his workshop talking about the different types of big data that is being used today. While obtaining and computing data might sound like something that is found strictly in a life or hard science field, big data can actually affect several areas including social sciences. As researchers continue to work together collecting, analyzing, and presenting data, having an area for everyone to access that data becomes a problem.
During research performed by groups at Microsoft, they realized that data intensive research is the same, no matter what discipline. Collection, viewing with colleagues, analyzing data, disseminating information, and then publishing and preserving the information is the key. However, Jim Gray, who began the push for data to be online, believed that research has come to the fourth paradigm where all scientific data is online so that all can use it. Today, that term is now called Big Data. Gray discovered after working with several groups that they all faced the same generic problems of getting, managing, organizing, and sharing data as well as executing models, visualizing, documenting, and curating the end result data. Also getting data recorded by big scientific groups into the hands of the lone graduate student is also a challenge.
“What’s interesting if you publish data and make it freely available to everybody, so truly open, the people who use this data are not necessarily the ones you think of” – Dr. Kenji Takeda
Managing and protecting the massive amounts of data that is collected is only a small part of the challenges being faced. The biggest challenge in doing something with that data and turning it into insight, especially actionable insight. Microsoft worked with Shoothill Environment Agency in order to take the U.K. Environment Agency river level data and turn it into a usable information. The result was an interactive map that sends out alerts to local residents and authorities in order to warn of potential flooding in the area. This was also one of the U.K.’s first Facebook apps that would send notices to residents upon request.
The cloud is a large way that life scientists are able to conduct their work without spending a lot of money. Ten years ago sequencing the human genome was very expensive; today sequencing a human’s genome costs roughly $1,000. Cloud computing gives anyone access to huge amounts of computing power.
Microsoft has a global cloud resource, known as Azure. This scalable resource allows researchers to store and analyze their data in a fraction of the time. Instead of spending weeks or months processing the information points obtained through tests and studies, Azure can process the massive amounts of data within a matter of days.
Big data is a part of research. Being able to not only share that data easily with others in the field, but also analyze that information is an important step. Cloud providers like Microsoft’s Azure, allow scientists to easily share and store that information as well as cut the computing time drastically.