Students for a Smarter Planet ..leaders with conscience
U Missouri
October 21st, 2014

How Medicare Data influcence our life? When you get hurted (which we don’t wish to happen), how will Medicare system take care of you? To what extent will Medicare System pay you? What is the high cost and low cost contributors to the Medicare system? In our project, we will do researchs on the real world Medicare claims data to get valuable insights on both the data and on parties who are involved in it like healthcare system.

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October 16th, 2014

New awards!

University of Missouri, US – 2 projects:
“RaW”, led by Chao Fang, finding protein substructures to enhance knowledge of protein function using analytics!
“DORK”, led by Xinjian Yao, using analytics to understand Medicare data to further understand what happens in the system.

University Putra Malaysia: Embedded Systems for Public Water Management, led by BALAMI Emmanuel Luke, toword making water supplies more sustainable.

Lumbini Engineering College, Nepal – event held September 28 to inform students about Smarter Planet, Bluemix, Watson, IBM and Students for a Smarter Planet opportunities. Led by Basant Pandey.

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January 15th, 2014

Here’s an article about our teams in Missouri.

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December 22nd, 2013

Posted by
Zhaoyu in

Our project – Real-time Emotion Analysis on Twitter – has completed. We are thinking about the extensibility of our project and we get some ideas.

We use spout and bolt in our project to process data. Spout is in master node and it is like the data file input in Hadoop. Bolt is in worker node and it is like the worker in Hadoop. In our project, the spout is to read Twitter Streaming data and send it to several bolts, which is the process of mapper. And bolt can send data to another bolt. So it is like a chain. Whenever we want to add some new features into our system, you just need to write a new bolt and add it into the processing chain.



In our project, there are two kinds of bolts. The first kind of bolt is to analyze the tweet and extract the useful information and add the emotion value. The the data will be sent to the next bolt, which is responsible to collect and gather the data from several source bolts and publish them into a Redis channel.

So here comes our scalability and extensibility. For the tweet analysis, if we also want to analyze the hashtags as well, all we need to do is to add a kind of bolt in the chain. Then you can either send the result to the reducer or just send the result into another redis channel.

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November 14th, 2013

Susan Zeng, an IBMer and professor at U Missouri brought us 2 projects from the Computer Science Department.

One is “Real-time Emotion Analysis On Twitter” – where students will attempt to analyze tweets to produce reports on feelings to help business people enhance services and products.

The other is “Identifying Gene Duplications Across DNA Sequences” – since these genes are associated with cancer, information about this could be useful as a step in helping find cures.

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