Data Mining Chapter 5 Part 1
Explore Data Mining Concepts
Test your knowledge on Data Mining with our comprehensive quiz focusing on Chapter 5. This quiz includes 25 multiple-choice questions designed to challenge your understanding and retention of key concepts related to the star-cubing algorithm, High-Dimensional OLAP, and data cube computations.
Whether you’re a student, teacher, or data enthusiast, you’ll find this quiz engaging and informative. Get ready to:
- Enhance your grasp of data mining techniques
- Challenge yourself with thought-provoking questions
- Apply what you’ve learned in a practical context
star-cubing operates data structure called a
Star-tree
Spanning tree
Binary tree
Syntax-tree
To explain how star-cubing algorithm works,we need to get :
Cuboid tree
Star-nodes
Star-trees
All of the above
If a single-dimensional aggregate on an attribute value p does not satisfy the iceberg condition, it called
Star-tree
Star-node
Star attribute
None
To compute Iceberg cubes we use
Multi-Way Array Aggregation Method
BUC
Star-cubing
All of the above
What is High-Dimensional OLAP (HOLAP)?
A database management system that uses a high number of dimensions to store data
A multidimensional database that can handle large amounts of data
An OLAP technology that combines the benefits of both ROLAP and MOLAP
A type of OLAP that uses a columnar data storage format
What is the main advantage of HOLAP over ROLAP?
. Faster query response times
Ability to handle larger data volumes
More flexible data modeling capabilities
Improved data consistency and accuracy
Which of the following is an example of a HOLAP tool?
Microsoft Excel
Oracle Database
MicroStrategy
Apache Hadoop
What is the main disadvantage of using HOLAP?
Limited scalability and performance issues
Complex implementation and maintenance requirements
Inability to handle unstructured data
Higher storage requirements compared to ROLAP
Computing a cuboid from the smallest, previously computed cuboid ?
Smallest-child
Cache-results
Amortize-scans
Share-sorts
Caching results of a cuboid from which other cuboids are computed to reduce disk I/Os ?
Share-sorts
Smallest-child
Cache-results
Amortize-scans
Computing as many as possible cuboids at the same time to amortize disk reads ?
Share-partitions
Share-sorts
Cache-results
Amortize-scans
Sharing costs cross multiple cuboids when sort-based method is used ?
Share-partitions
Share-sorts
Cache-results
Amortize-scans
Sharing cost across multiple cuboids when hash-based algorithms are used ?
Share-partitions
Share-sorts
Cache-results
Amortize-scans
Top-down model explores the notation of shared dimensions.
True
False
Star-cubing is able to prune the indicated cuboids because it considers shared dimensions
True
False
A naïve approach to computing on Iceberg cube would be first compute the full cube and prune the cells that do not satisfy the Iceberg condition.
True
False
Star-cubing integrates top-down and bottom-up cube computation
True
False
Sharing the partitioning costs cross multiple cuboids when sort-based method is used
True
False
BUC is an algorithm that constructs the cube from the base cuboid toward the apex cuboid and cannot prune during construction
True
False
BUC is an algorithm that can use either a top-down or a bottom-up approach to explore a 3-D data cube
True
False
The multiway array aggregation (or simply MultiWay) method computes a full data cube by using a multidimensional array
True
False
The Multi-Way Array Aggregation is an array-based "bottom-up" algorithm
True
False
In multiway array aggregation algorithm , there is no different approach developed for the array-based cube construction
True
False
In data cube To roll up, we move from the apex cuboid downward in the lattice
True
False
A data cube of n dimensions contains 2^n cuboids
True
False
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