Distributed Data Mining: The JAM system architecture
Andreas L. Prodromidis, Salvatore J. Stolfo, Shelley Tselepis, Terrance Truta, Jeffrey Sherwin, David Kalina · 2001
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.