Multidimensional cyclic graph approach : representing a data cube without common sub-graphs.

Submitted by Oliveira Flávia (flavia@sisbin.ufop.br) on 2015-01-22T20:00:19Z No. of bitstreams: 1 ARTIGO_MultidimensionalCyclicGraph.pdf: 2678344 bytes, checksum: 8501a62bb9391990f19502a52ee9260d (MD5)Approved for entry into archive by Gracilene Carvalho (gracilene@sisbin.ufop.br) on 2015-01-26T11:25:29Z (GMT) No. of bitstreams: 1 ARTIGO_MultidimensionalCyclicGraph.pdf: 2678344 bytes, checksum: 8501a62bb9391990f19502a52ee9260d (MD5)Made available in DSpace on 2015-01-26T11:25:29Z (GMT). No. of bitstreams: 1 ARTIGO_MultidimensionalCyclicGraph.pdf: 2678344 bytes, checksum: 8501a62bb9391990f19502a52ee9260d (MD5) Previous issue date: 2011We present a new full cube computation technique and a cube storage representation approach, called the multidimensional cyclic graph (MCG) approach. The data cube relational operator has exponential complexity and therefore its materialization involves both a huge amount of memory and a substantial amount of time. Reducing the size of data cubes, without a loss of generality, thus becomes a fundamental problem. Previous approaches, such as Dwarf, Star and MDAG, have substantially reduced the cube size using graph representations. In general, they eliminate prefix redundancy and some suffix redundancy from a data cube. The MCG differs significantly from previous approaches as it completely eliminates prefix and suffix redundancies from a data cube. A data cube can be viewed as a set of sub-graphs. In general, redundant sub-graphs are quite common in a data cube, but eliminating them is a hard problem. Dwarf, Star and MDAG approaches only eliminate some specific common sub-graphs. The MCG approach 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. In addition, it is computed for any measurement type: distributive, algebraic or holistic. MCG performance analysis demonstrates that MCG is 20–40% faster than Dwarf, Star and MDAG approaches when computing sparse data cubes. Dense data cubes have a small number of aggregations, so there is not enough room for runtime and memory consumption optimization, therefore the MCG approach is not useful in computing such dense cubes. The compact representation of sparse data cubes enables the MCG approach to reduce memory consumption by 70–90% when compared to the original Star approach, proposed in [33]. In the same scenarios, the improved Star approach, proposed in [34], reduces memory consumption by only 10–30%, Dwarf by 30–50% and MDAG by 40–60%, when compared to the original Star approach. The MCG is the first approach that uses an exact sub-graph matching function to reduce cube size, avoiding unnecessary aggregation, i.e. improving cube computation runtime