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Data to Knowledge (D2K)

August 1, 2006

Loretta Auvil, NCSA
11:00 AM to 12:30 PM (PDT)
1:00 PM to 2:30 PM (CDT)
Room 3000 of the NCSA Building, 1205 W. Clark St., Urbana, IL [Driving Directions]
Webcast: www.cichannel.org
AG Venue: http://agschedule.ncsa.uiuc.edu/meetingdetails.asp?MID=15940

Description:

With the tremendous growth in data capture, researchers are recognizing the need for knowledge and learning frameworks that can handle distributed data storage systems as well provide the means for distributed data analysis. Applying data analysis to practical problems requires deployment of a system that offers tools for both centralized and distributed knowledge discovery. This system should target the heart of the research activity, the knowledge discovery process, not just the end result. It should provide a means for locating datasets, retrieving data, segmenting the data into testing and training sets, data mining, and the publishing of results into a searchable repository. To address these needs, the NCSA's Automated Learning Group has developed the D2K - Data to Knowledge - analysis framework.

D2K - Data to Knowledge is a rapid, flexible data mining and machine learning system that integrates analytical data mining methods for prediction, discovery, and deviation detection, with data and information visualization tools. It offers a visual programming environment that allows users to connect programming modules together to build data mining applications and supplies a core set of modules, application templates, and a standard API for software component development. All D2K components are written in Java for maximum flexibility and portability. This seminar will provide an overview of D2K and its many capabilities.

References:

D2K - http ://alg.ncsa.uiuc.edu/do/tools/d2k

Eclipse - http://www.eclipse.org/downloads/

NetBeans - http://www.netbeans.org/downloads/index.html

Point of Contact:

For questions regarding this training event, contact the NCSA Training Group.

The CyberInfrastructure Partnership is funded by the National Science Foundation and includes the following partners: NCSA and SDSC.