Question

There are several typical cube computation methods such as Multi-Way, BUC, and Star-cubing. Briefly describe each one of these methods outlining the key points.

I need a unique answer and without handwriting, please.

Answer 1

Data cube computation are of four types:-

-Multi-way array aggregation:-

* It is a array based bottom-up algorithm.

* It is a typical MOLAP approach that uses direct array addressing.

* It uses simultaneous aggregation on multiple dimensions.

* Partition arrays into chunks i.e a small sub cube which fits in the memory.

-BUC computation:-

* BUC is also known as bottom -up cube computation.

* If a partition does not satisfy min-sup, its descendent can be pruned.

* It has no simultaneous aggregation.

* Partitioning uses external sorting, hashing, counting sort.

-Star cubing computation:-

*Aggregate in a top-down manner but with the bottom-up sub-layer underneath which will allow Apriori pruning.

* Allows for shared computation.

* Shared dimensions grow in bottom-up fashion

* Anti-monotonic property for shared dimensions

- High dimensional OLAP

* OLAP is a minimal cubing approach.

* It is known as the 'curse of dimensionality' problem.

* None of the previous cubing can handle the high dimensionality.

* High -D OLAP is used in applications such as science and engineering analysis, bio-data analysis, statistical surveys etc.

* Partitions the set of dimensions into shell fragments.

*Partitions the data vertically.