Consider that the visualization of a 3-dimensional data cube is showing Sales Quantity for each combination of the attributes Product Type, Month and Country.
From this, if we want to further visualize the Sales Quantity for each combination of Product Type, Month and State, which of the following OLAP operations should be performed?

Question

Consider performing depth-first search (DFS) on an undirected and unweighted graph G starting at vertex s. For any vertex u in G, d[u] is the length of the shortest path from s to u. Let (u, v) be an edge in G such that d[u] < d[v]. If the edge (u, v) is explored first in the direction from u to v during the above DFS, then (u, v) becomes a _____ edge.

Answer 1

This question tests the four basic OLAP (Online Analytical Processing) operations used on a multidimensional data cube: slicing, dicing, roll-up and drill-down. Let's check each option against what the question describes.

Slicing: this operation fixes a single dimension at one value and views the remaining lower-dimensional cube, such as looking only at Country = "USA". The question does not fix any dimension to one value, so this does not fit.
Dicing: this operation selects a sub-cube by fixing a specific range of values on two or more dimensions at once. The question is not restricting to particular values of any dimension either, so this does not fit.
Roll-up: this operation climbs up a concept hierarchy, aggregating detailed data into a more summarized form, for example moving from State-wise sales to Country-wise sales. The question moves in the opposite direction, from Country to State, so roll-up is wrong here.
Drill-down: this operation is the reverse of roll-up. It moves down a concept hierarchy from a summarized level to a more detailed level, for example Country breaking down into its States. The question exactly describes going from Country-level sales data to State-level sales data, which is a move to finer detail.

Since Country and State belong to the same geography hierarchy, with State being a lower, more detailed level than Country, moving from Country to State is a Drill-down operation.

Let's summarize:

Slicing and Dicing change which values of a dimension are shown, not the level of detail.
Roll-up moves to a coarser level, Drill-down moves to a finer level, and here the request is for finer detail.

So the operation needed is Drill-down.

Answer 2

This question tests the four basic OLAP (Online Analytical Processing) operations used on a multidimensional data cube: slicing, dicing, roll-up and drill-down. Let's check each option against what the question describes.

Slicing: this operation fixes a single dimension at one value and views the remaining lower-dimensional cube, such as looking only at Country = "USA". The question does not fix any dimension to one value, so this does not fit.
Dicing: this operation selects a sub-cube by fixing a specific range of values on two or more dimensions at once. The question is not restricting to particular values of any dimension either, so this does not fit.
Roll-up: this operation climbs up a concept hierarchy, aggregating detailed data into a more summarized form, for example moving from State-wise sales to Country-wise sales. The question moves in the opposite direction, from Country to State, so roll-up is wrong here.
Drill-down: this operation is the reverse of roll-up. It moves down a concept hierarchy from a summarized level to a more detailed level, for example Country breaking down into its States. The question exactly describes going from Country-level sales data to State-level sales data, which is a move to finer detail.

Since Country and State belong to the same geography hierarchy, with State being a lower, more detailed level than Country, moving from Country to State is a Drill-down operation.

Let's summarize:

Slicing and Dicing change which values of a dimension are shown, not the level of detail.
Roll-up moves to a coarser level, Drill-down moves to a finer level, and here the request is for finer detail.

So the operation needed is Drill-down.

Column 1		Column 2
(p)	First In First Out	(i)	Stacks
(q)	Lookup Operation	(ii)	Queues
(r)	Last In First Out	(iii)	Hash Tables

Show Hint

The Correct Option is D

Solution and Explanation

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