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
Proceedings of the 2002 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
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach
IEEE Transactions on Knowledge and Data Engineering
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Star-cubing: computing iceberg cubes by top-down and bottom-up integration
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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. In a data cube, each cuboid can be viewed as a set of sub-graphs. In general, redundant sub-graphs are quite common in a data cube, but their elimination is a hard problem as some previous cube approaches demonstrate. The MCG approach differentiates significantly from previous approaches since it efficiently eliminates all common sub-graphs from the entire cube, based on an exact sub-graph matching solution. We propose a matching function to guarantee one-to-one mapping between sub-graphs. The function is computed incrementally, in a top-down fashion, and its computation uses a minimal amount of information to generate unique results, regardless of whether we are using distributive, algebraic or holistic measures. MCG performance analysis demonstrates a similar runtime when compared to Star approach and very low memory consumption (94--98% reduction) when compared to a full cube representation.