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http://wiki.apache.org/lucenehadoop/Hbase/ShellPlans

Group <99%>'''Group''' tuples by value of an attribute and apply aggregate function
independently to each group of tuples.[[BR]]'''Aggregate Functions''' : ~''AVG( attribute
), SUM( attribute ), COUNT( attribute ), MIN( attribute ), MAX( attribute )''~[[BR]][[BR]]~''A
= Table('movieLog_table);[[BR]]B = A.Group('studioName', MIN('year')); '''//γ,,studioName.MIN(
year ),,(A)''' ''~ 
Sort <99%>'''Sort''' of tuples(rows) of R, ordered according to columnfamilies
on columnfamilylist.[[BR]][[BR]]~''A = Table('movieLog_table');[[BR]]B = Sort A by ('length');
'''//τ,,length,,(A)''' ''~ 
 ''~Again, to help readers, you might cite pages that explain 'relational algebra' or examples
of its use in databases to help contextualize your plan (Aren't there other relational operators
than these that might be included? Do you intend to implement those? If not, you might
say why not of if you intend to do these as 'Matrix Arithmetic Operators, you might say so.
 St.Ack~''
+ '''(ex. 1) '''Search the subject and the year of the movies which were produced by 'Fox'
company and where running time is more than 100 minutes.
+ [[BR]]~''π ,,title.year,, (σ ,,length > 100,, (movieLog_table)∩σ ,,studioName =
'Fox',, (movieLog_table))''~
+
+ {{{
+ Hbase.altools > A = Table('movieLog_table');
+ Hbase.altools > B = A.Projection('year');
+ Hbase.altools > C = B.Selection(length > 100 AND studioName = 'Fox');
+ }}}
+
+ '''(ex. 2) '''Referential Integrity (minimum set of two or more attributes)
+ [[BR]]~''π ,,movieTitle.movieYear,, (movieStars_table) ⊆ π ,,title.year,, (movieLog_table)''~
+
+ {{{
+ example
+ }}}
==== Matrix Arithmetic Operators ====
<bgcolor="#E5E5E5">'''Operator''' <bgcolor="#E5E5E5">'''Explanation''' 
@@ 107, +121 @@
QR <99%>'''QR Decomposition'''[[BR]]For an mbyn matrix A with m >= n, the
QR decomposition is an mbyn orthogonal matrix Q and an nbyn upper triangular matrix R
so that A = Q*R.[[BR]]'''Functions''' : ~''getH(), getQ(), getR()''~[[BR]][[BR]]~''A =
Matrix('m_table','cf_1');[[BR]]B = QRDecomposition(A);[[BR]]C = getH(B);''~
Cholesky <99%>'''Cholesky Decomposition'''[[BR]]It is a special case of LU decomposition
applicable only if matrix to be decomposed is symmetric positive definite.[[BR]]'''Functions'''
: ~''getL(), isSPD()''~ [[BR]][[BR]]~''A = Matrix('m_table','cf_1');[[BR]]B = CholeskyDecomposition(A);[[BR]]C
= getU(B);[[BR]]D = getL(A);''~
SVD <99%>'''SV(Singular Value) Decomposition'''[[BR]]For an mbyn matrix A with
m >= n, the singular value decomposition is an mbyn orthogonal matrix U, an nbyn diagonal
matrix S, and an nbyn orthogonal matrix V so that A = U*S*V'.[[BR]]'''Functions''' : ~''getS(),
getU(), getV(), getSingularValues()''~ [[BR]][[BR]]~''A = Matrix('m_table','cf_1');[[BR]]B
= SVDecomposition(A);[[BR]]C = getU(B);''~
+
+ ''~Again, to help readers, you might cite pages that explain 'relational algebra' or examples
of its use in databases to help contextualize your plan (Aren't there other relational operators
than these that might be included? Do you intend to implement those? If not, you might
say why not of if you intend to do these as 'Matrix Arithmetic Operators, you might say so.
 St.Ack~''

= Implementation =
