Multiway Array Aggregation For Full Cube Computation


The Multiway Array Aggregation (or simply MultiWay) method computes a full data cube by using a multidimensional array as its basic data structure. It is a typical MOLAP approach that uses direct array addressing, where dimension values are accessed via the position or index of their corresponding array locations. Hence, MultiWay cannot perform any value-based reordering as an optimization technique. A different approach is developed for the array-based cube construction, as follows:


1.       Partition the array into chunks. A chunk is a sub cube that is small enough to fit into the memory available for cube computation. Chunking is a method for dividing an n-dimensional array into small n-dimensional chunks, where each chunk is stored as an object on disk. The chunks are compressed so as to remove wasted space resulting from empty array cells (i.e., cells that do not contain any valid data, whose cell count is zero). For instance, “chunkID offset” can be used as a cell addressing mechanism to compress a sparse array structure and when searching for cells within a chunk. Such a compression technique is powerful enough to handle sparse cubes, both on disk and in memory.

2.       Compute aggregates by visiting (i.e., accessing the values at) cube cells. The order in which cells are visited can be optimized so as to minimize the number of times that each cell must be revisited, thereby reducing memory access and storage costs. The trick is to exploit this ordering so that partial aggregates can be computed simultaneously, and any unnecessary revisiting of cells is avoided.

Because this chunking technique involves “overlapping” some of the aggregation computations, it is referred to as multiway array aggregation. It performs simultaneous aggregation—that is, it computes aggregations simultaneously on multiple dimensions.


BUC: Computing Iceberg Cubes from the Apex Cuboid Downward

BUC is an algorithm for the computation of sparse and iceberg cubes. Unlike Multi Way, BUC constructs the cube from the apex cuboid toward the base cuboid. This allows BUC to share data partitioning costs. This order of processing also allows BUCto prune during construction, using the Apriori property.

Figure 4.1 shows a lattice of cuboids, making up a 3-D data cube with the dimensions A, B, and C. The apex (0-D) cuboid, representing the concept all (that is, (



)), is at the top of the lattice. This is the most aggregated or generalized level. The 3-D base cuboid, ABC, is at the bottom of the lattice. It is the least aggregated (most detailed or specialized) level. This representation of a lattice of cuboids, with the apex at the top and the base at the bottom, is commonly accepted in data warehousing. It consolidates the notions of drill-down (where we can move from a highly aggregated cell to lower, more detailed cells) and roll-up (where we can move from detailed, low-level cells to higher level, more aggregated cells).

BUC stands for “Bottom-Up Construction.” However, according to the lattice convention described above and used throughout this book, the order of processing of BUC is actually top-down! The authors of BUC view a lattice of cuboids in the reverse order, with the apex cuboid at the bottom and the base cuboid at the top. In that view, BUC does bottom-up construction. However, because we adopt the application worldview where drill-down refers to drilling from the apex cuboid down toward the base cuboid, the exploration process of BUC is regarded as top-down. BUC’s exploration for the computation of a 3-D data cube is shown in Figure 4.5.

The BUC algorithm is shown in Figure 4.6. We first give an explanation of the algorithm and then follow up with an example. Initially, the algorithm is called with the input relation (set of tuples). BUC aggregates the entire input (line 1) and writes


Algorithm: BUC. Algorithm for the computation of sparse and iceberg cubes.