Distributed Data Mining: The JAM system architecture

Andreas L. Prodromidis; Salvatore Stolfo; Shelley Tselepis; Terrance Truta; Jeffrey Sherwin; David Kalina

Distributed Data Mining: The JAM system architecture
Prodromidis, Andreas L.
Stolfo, Salvatore
Tselepis, Shelley
Truta, Terrance
Sherwin, Jeffrey
Kalina, David
Technical reports
Computer Science
Permanent URL:
Columbia University Computer Science Technical Reports
Part Number:
Department of Computer Science, Columbia University
Publisher Location:
New York
This paper describes the system architecture of JAM (Java Agents for Meta-learning), a distributed data mining system that scales up to large and physically separated data sets. An earlyversion of the JAM system was described in Stolfo-et-al-97-KDD-JAM. Since then, JAM has evolved both architecturally and functionally and here we present the final design and implementation details of this system architecture. JAM is an extensible agent-based distributed data mining system that supports the remote dispatch and exchange of agents among participating datasites and employs meta-learning techniques to combine the multiple models that are learned. One of JAM's target applications is fraud and intrusion detection in financial information systems. A brief description of this learning task and JAM's applicability and summary results are also discussed.
Computer science
Item views:
Additional metadata is currently unavailable for this item.
Suggested Citation:
Andreas L. Prodromidis, Salvatore Stolfo, Shelley Tselepis, Terrance Truta, Jeffrey Sherwin, David Kalina, 2001, Distributed Data Mining: The JAM system architecture, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:29261.

In Partnership with the Center for Digital Research and Scholarship at Columbia University Libraries | Terms of Use | Copyright