Distributed Data Mining Tasks and Patterns as Services
Euro-Par 2008 Workshops - Parallel Processing
How distributed data mining tasks can thrive as knowledge services
Communications of the ACM
Distributed data mining patterns and services: an architecture and experiments
Concurrency and Computation: Practice & Experience
A service oriented architecture to provide data mining services for non-expert data miners
Decision Support Systems
SMINER - a platform for data mining based on service-oriented architecture
International Journal of Business Intelligence and Data Mining
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The service-oriented architecture paradigm can be exploited for the implementation of data and knowledge-based applications in distributed environments. The Web services resource framework (WSRF) has recently emerged as the standard for the implementation of Grid services and applications. WSRF can be exploited for developing high-level services for distributed data mining applications. This paper describes Weka4WS, a framework that extends the widely used open source Weka toolkit to support distributed data mining on WSRF-enabled Grids. Weka4WS adopts the WSRF technology for running remote data mining algorithms and managing distributed computations. The Weka4WS user interface supports the execution of both local and remote data mining tasks. On every computing node, a WSRF-compliant Web service is used to expose all the data mining algorithms provided by the Weka library. The paper describes the design and implementation of Weka4WS using the WSRF libraries and services provided by Globus Toolkit 4. A performance analysis of Weka4WS for executing distributed data mining tasks in different network scenarios is presented. Copyright © 2008 John Wiley & Sons, Ltd.