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Distributed Data Mining: The JAM system architecture

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

Title:
Distributed Data Mining: The JAM system architecture
Author(s):
Prodromidis, Andreas L.
Stolfo, Salvatore
Tselepis, Shelley
Truta, Terrance
Sherwin, Jeffrey
Kalina, David
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
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
Item views:
316
Metadata:
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