An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach
IEEE Transactions on Knowledge and Data Engineering
High-dimensional OLAP: a minimal cubing approach
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Multidimensional cyclic graph approach: Representing a data cube without common sub-graphs
Information Sciences: an International Journal
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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.