Distributed Data Mining: The JAM system architecture

Technical reports:

Title:: Distributed Data Mining: The JAM system architecture
Author(s):: Prodromidis, Andreas L.
Stolfo, Salvatore
Tselepis, Shelley
Truta, Terrance
Sherwin, Jeffrey
Kalina, David
Date:: 2001
Type:: Technical reports
Department:: Computer Science
Permanent URL:: http://hdl.handle.net/10022/AC:P:29261
Series:: Columbia University Computer Science Technical Reports
Part Number:: CUCS-007-01
Publisher:: Department of Computer Science, Columbia University
Publisher Location:: New York
Abstract:: 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.
Subject(s):: Computer science