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What are the techniques for Data Cube Computations?
The following are general optimization techniques for efficient computation of data cubes which as follows −
Sorting, hashing, and grouping − Sorting, hashing, and grouping operations must be used to the dimension attributes to reorder and cluster associated tuples. In cube computation, aggregation is implemented on the tuples that share the similar set of dimension values. Therefore, it is essential to analyse sorting, hashing, and grouping services to access and group such data to support evaluation of such aggregates.
It can calculate total sales by branch, day, and item. It can be more effective to sort tuples or cells by branch, and thus by day, and then group them as per the item name. An effective performance of such operations in huge data sets have been widely considered in the database research community.
Such performance can be continued to data cube computation. This method can also be continued to implement shared-sorts (i.e., sharing sorting costs across different cuboids when sort-based techniques are used), or to implement shared-partitions (i.e., sharing the partitioning cost across different cuboids when hash-based algorithms are utilized).
Simultaneous aggregation and caching of intermediate results − In cube computation, it is effective to calculate higher-level aggregates from earlier computed lower-level aggregates, instead of from the base fact table. Furthermore, simultaneous aggregation from cached intermediate computation results can lead to the decline of high-priced disk input/output (I/O) operations.
It can compute sales by branch, for instance, it can use the intermediate results changed from the computation of a lower-level cuboid including sales by branch and day. This methods can be continued to implement amortized scans (i.e., computing as several cuboids as possible simultaneously to amortize disk reads).
Aggregation from the smallest child when there exist multiple child cuboids − When there exist several child cuboids, it is generally more effective to calculate the desired parent (i.e., more generalized) cuboid from the smallest, formerly computed child cuboid.
The Apriori pruning method can be explored to compute iceberg cubes efficiently − The Apriori property in the context of data cubes, defined as follows: If a given cell does not fulfil minimum support, therefore no descendant of the cell (i.e., more specific cell) will satisfy minimum support. This property can be used to largely decrease the computation of iceberg cubes.
The description of iceberg cubes includes an iceberg condition, which is a constraint on the cells to be materialized. A general iceberg condition is that the cells should satisfy a minimum support threshold including a minimum count or sum. In this term, the Apriori property can be used to shorten away the inspection of the cell’s descendants.
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