Issue No. 06 - November/December (2008 vol. 12)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIC.2008.122
Vlado Stankovski , University of Ljubljana
Martin Swain , University of Ulster
Valentin Kravtsov , Technion?Israel Institute of Technology
Thomas Niessen , Scopevisio AG
Dennis Wegener , Fraunhofer Institute for Intelligent Analysis and Information Systems
Matthias R? , DaimlerChrysler
Jernej Trnkoczy , University of Ljubljana
Michael May , Fraunhofer Institute for Intelligent Analysis and Information Systems
J? Franke , DaimlerChrysler
Assaf Schuster , Technion?Israel Institute of Technology
Werner Dubitzky , University of Ulster
The growing computerization in modern knowledge and technology sectors is generating huge volumes of electronically stored data. Data mining technology is often employed to make sense of these data. However, as modern data mining applications increase in complexity, so do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, we developed the DataMiningGrid system, which principally differs from similar systems by its ability to integrate a diverse set of programs and application scenarios within a single framework. The system's key features include high performance and scalability, sophisticated support for relevant standards, different user types, and flexible extensibility. The software is available as open source.
distributed architectures, distributed applications, data mining, middleware/business logic
W. Dubitzky et al., "Digging Deep into the Data Mine with DataMiningGrid," in IEEE Internet Computing, vol. 12, no. , pp. 69-76, 2008.