2012 雲端計算專題構想(2) - Big Data Analytics with the Cloud

Instructor
Shih-Hao Hung

Contact
hungsh@csie.ntu.edu.tw

Project title
Big Data Analytics with the Cloud

Project description
Big data analysis is a hot topic. To make use of big data, we are looking the following three important layers in the system:

(1) Application domain: Smart ideas to produce, analyze, and use big data for specific applications, and then implement the ideas with the right cloud infrastructure.This is best done by someone with the knowledge of the application domain.

(2) Cloud Middleware: This is for someone who would like to look deeper into the cloud. Cloud middleware is very critical to big data analytics. People keep develop middleware to make the analysis of big data easier and more efficient. What are the tricks behind those middleware?

(3) Cloud Architecture: How would you store big data? How would you connect the machines? These are the questions you would ask if you are setting up your own cloud for big data applications.

In our lab, we are working with the industry for all three layers. We think that the best way for you to learn about big data analytics is to do a team project and work with others.

Project deliverable
(1) New system enhancing techniques with implementation, or
(2) New collaborative Android applications based our framework.

Prerequisites
The prospective participants who are interested in Part (1) of this project should be familiar with the basic concept of cloud computing, parallel programming, and distributed computing. Those who are interested in Part (2) should be familiar with UNIX system programming, operating system, and algorithm design and analysis. Those who are interested in Part (3) should be familiar with operating system, and computer architecture.

Number of students required.
1~4 students

Meeting schedule
The students are expected to meet with all project members at least once every two weeks.

Progress report
The students are expected to file progress report using a Google document.

Grading criteria
Survey of existing work 25%
Innovation 25%
Implementation 25%
Performance Evaluation 25%