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3 . 2 2 . When computing a cube of high dimensionality, we encounter the inherent curse of dimensionality problem: There exists a huge number

3.22. When computing a cube of high dimensionality, we encounter the inherent curse of
dimensionality problem: There exists a huge number of subsets of combinations of dimensions.
(a) Suppose that there are only two base cells, {(a1, a2, a3,......., a100) and (a1, a2, b3,........., b100)},
in a 100-D base cuboid. Compute the number of nonempty aggregate cells. Comment on the
storage space and time required to compute these cells.
(b) Suppose we are to compute an iceberg cube from(a). If the minimum support count in the
iceberg condition is 2, how many aggregate cells will there be in the iceberg cube? Show the
cells.
(c) Introducing iceberg cubes will lessen the burden of computing trivial aggregate cells in a data
cube. However, even with iceberg cubes, we could still end up having to compute a large
number of trivial uninteresting cells (i.e., with small counts). Suppose that a database has 20
tuples that map to (or cover) the two following base cells in a 100-D base cuboid, each with a
cell count of 10: {( a1, a2, a3,......., a100): 10,(a1, a2, b3,........., b100): 10}.
i. Let the minimum support be 10. How many distinct aggregate cells will there be
like the following: {( a1, a2, a3, a4,......., a99,*): 10,........,(a1, a2,*, a4,.........,a99,
a100): 10,.......,( a1, a2, a3,*,.......,*,*): 10}.
ii. If we ignore all the aggregate cells that can be obtained by replacing some
constants with *s while keeping the same measure value, how many distinct
cells remain? What are the cells?

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