A multi-tier framework for dynamic data collection, analysis, and visualization

This thesis describes a framework for collecting, analyzing, and visualizing dynamic data, particularly data gathered through Web questionnaires. The framework addresses challenges such as promoting user participation, handling missing or invalid data, and streamlining the data interpretation process. Tools in the framework provide an intuitive way to build robust questionnaires on the Web and perform on-the-fly analysis and visualization of results. A novel 2.5-dimensional dynamic response-distribution visualization allows subjects to compare their results against others immediately after they have submitted their response, thereby encouraging active participation in ongoing research studies. Other modules offer the capability to quickly gain insight and discover patterns in user data. The framework has been implemented in a multi-tier architecture within an open-source, Java-based platform. It is incorporated into Risk Psychology Network, a research and educational project at MIT's Laboratory for Financial Engineering.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Ke, Xian, 1981-

Advisor dc:contributor.advisor

• Andrew W. Lo and Dmitry Repin.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/28416

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/28416