Computing Data Cubes without Redundant Aggregated Nodes and Single Graph Paths: The Sequential MCG Approach

In this paper, we present a novel full cube computation and representation approach, named MCG. A data cube can be defined as a lattice of cuboids. In our approach, each cuboid is seen as a set of sub-graphs. Redundant suffixed nodes in such sub-graphs are quite common, but their elimination is a hard problem as some previous cube approaches demonstrate. MCG approach computes a data cube in two phases: First, it generates a base cuboid from a base relation with no tuples rearrangement. Second, it generates all the remaining aggregated cells, in a top-down fashion, with a unique base-MCG scan. During both MCG cube computation phases, the MCG cube size reduction method maintains the entire lattice of cuboids without common prefixed nodes and common single graph paths. During the second phase, the reduction method also eliminates common aggregated nodes that are normally frequent when sparse relations are computed. MCG performance analysis demonstrates an efficient runtime and very low memory consumption when compared to Star and MDAG full cube approaches.