Special section: Data mining in grid computing environments

The challenges involved, applications developed, and lessons learned from efforts in bringing data mining in to modern grid computing and Web services environments, are explored. Grid environments refers to persistent computing environments that enable software applications to integrate instruments, displays, and information resources that are managed by diverse organizations in widespread locations. Natarajan, Sion, Apte, and Narang described a grid-based approach to enterprise-scale data mining. This approach relies on a simple algorithmic decomposition of the data mining kernel on the data and computational grids, while minimizing the data transfer between them. Cannataro, Guzzi, Mazza, and Veltri also proposed the use of ontologies to describe the semantics of basic pre-processing services and other data mining applications and to guide biologists in formulating their problems and choosing possible solutions and related services.