How distributed data mining tasks can thrive as knowledge services
Communications of the ACM
Open workflow infrastructure: a research agenda
Proceedings of the 1st International Workshop on Workflow Approaches to New Data-centric Science
Ubiquitous knowledge discovery
Ubiquitous knowledge discovery
Distributed data mining for e-business
Information Technology and Management
An equation-discovery approach to earthquake-ground-motion prediction
Engineering Applications of Artificial Intelligence
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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.