By Lin D. Y.
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Extra info for [Article] Semiparametric regression analysis of longitudinal data with informative drop-outs
DWD SODFHPHQW DQG D GLVWULEXWHGVSDWLDODFFHVV PHWKRG Data placement is an important resource management issue in the shared-nothing parallel and distributed database system. Much excellent research has been conducted on both relational databases and spatial databases. All previous work used the GHFOXVWHULQJVWUDWHJ\ to place data among available computers. Declustering exploits ,2 parallelism but it also leads to higher communication cost. Declustering minimizes the query time for a single query.
But this approach pays a big communication overhead in the higher levels of the tree as it has to shuffle lots of training data items to different processors. Once every node is solely assigned to a single processor, each processor can construct the partial classification tree independently without any communication with other processors. However, the load imbalance problem is still present after the shuffling of the training data items, since the partitioning of the data was done statically. The hybrid approach combines the good features of these two approaches to reduce communication overhead and load imbalance.
However, the load imbalance problem is still present after the shuffling of the training data items, since the partitioning of the data was done statically. The hybrid approach combines the good features of these two approaches to reduce communication overhead and load imbalance. This approach uses the 6\QFKURQRXV 7UHH &RQVWUXFWLRQ$SSURDFKfor the upper parts of the classification tree. Since there are few nodes and relatively large number of the training cases associated with the nodes in the upper part of the tree, the communication overhead is small.
[Article] Semiparametric regression analysis of longitudinal data with informative drop-outs by Lin D. Y.