TY - JOUR
T1 - Digging deep into the data mine with DataMiningGrid
AU - Stankovski, V.
AU - Swain, M.
AU - Kravtsov, V.
AU - Niessen, T.
AU - Wegener, D.
AU - Kindermann, J.
AU - Dubitzky, W.
PY - 2008/11/11
Y1 - 2008/11/11
N2 - As modern data mining applications increase in complexity, so too 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, the authors developed the DataMiningGrid system. It integrates a diverse set of programs and application scenarios within a single framework, and features scalability, flexible extensibility, sophisticated support for relevant standards and different users.
AB - As modern data mining applications increase in complexity, so too 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, the authors developed the DataMiningGrid system. It integrates a diverse set of programs and application scenarios within a single framework, and features scalability, flexible extensibility, sophisticated support for relevant standards and different users.
UR - http://hdl.handle.net/2160/12379
U2 - 10.1109/MIC.2008.122
DO - 10.1109/MIC.2008.122
M3 - Article
SN - 1089-7801
VL - 12
SP - 69
EP - 76
JO - IEEE Internet Computing
JF - IEEE Internet Computing
IS - 6
ER -