Describe Star-Cubing method with suitable example.

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 The Star-Cubing method, which integrates top-down and bottom-up computation

  • Star Cubing integrates the top-down and bottom-up methods. It explores shared dimensions. 
  • Star-Cubing combines the strengths of the other methods we have studied up to this point. It integrates top-down and bottom-up cube computation and explores both multidimensional aggregation (similar to MultiWay) and Apriori-like pruning (similar to BUC). It operates from a data structure called a star tree, which performs lossless data compression, thereby reducing the computation time and memory requirements.
  • The Star-Cubing algorithm explores both the bottom-up and top-down computation models as follows: On the global computation order, it uses the bottom-up model. However, it has a sublayer underneath based on the top-down model, which explores the notion of shared dimensions, as we shall see in the following. This integration allows the algorithm to aggregate on multiple dimensions while still partitioning parent group-by's and pruning child group-by's that do not satisfy the iceberg condition.
  • If we were to follow only the bottom-up model (similar to MultiWay), then the cuboids marked as pruned by Star-Cubing would still be explored. Star-Cubing is able to prune the indicated cuboids because it considers shared dimensions.


For example, dimension A is the shared dimension of AC and C. AC/C means cuboid AC has shared dimensions C. Star cubing allows for shared computations. e.g., cuboid C is computed simultaneously as AC. Star Cubing aggregates in a top-down manner but with the bottom-up sub- layer underneath which will allow Apriori pruning. Its shared dimensions grow in a bottom-up fashion. As shown in figure 4.7.




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