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The matrix package will be useful for some of the LargeScale Numeric Analysis and Data
Mining which need the computation system of the Inverse Matrix for Data Mining related area
(e.g. linear regression, PCA, SVM, ..., etc).
 The current sharedmemory based parallel matrix solution provides a scalable and high performance
matrix operations, however, matrix resources can't be scalable. But, Using Hbase's Row,Column(Qualifier)
two dimensional space, we are able to store large sparse matrix. Also, The Autopartitioned
sparsity substructure will be efficiently managed and serviced by Hbase. Row or Column operations
can be done in linear time and algorithms such as structured Gaussian elimination or iterative
methods run in O(~the number of nonzero elements in the matrix~ / ~number of mappers (processors/cores)~)
time on Map/Reduce.
+ Generally, The current sharedmemory based parallel matrix solution provides a scalable
and high performance matrix operations, however, matrix resources can't be scalable. But,
Using Hbase's Row,Column(Qualifier) two dimensional space, we are able to store large sparse
matrix. Also, The Autopartitioned sparsity substructure will be efficiently managed and
serviced by Hbase. Row or Column operations can be done in linear time and algorithms such
as structured Gaussian elimination or iterative methods run in O(~the number of nonzero
elements in the matrix~ / ~number of mappers (processors/cores)~) time on Map/Reduce.
=== Initial Contributors ===
