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.
- 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
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