Data warehouse enhancement: A semantic cube model approach

Huang, Shi‐Ming; Chou, Tung‐Hsiang; Seng, Jia‐Lang

doi:10.1016/j.ins.2006.12.022

Cited by 28 publications

(15 citation statements)

References 32 publications

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“…In Information Sciences Journal two recent approaches [13,23] propose different strategies for analyzing multiple cubes. In [23] the authors use the concept of closed cells, explained in Section 2, to reduce the cube size.…”

Section: Discussionmentioning

confidence: 99%

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

Lima

Hirata

2011

Information Sciences

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a b s t r a c tWe 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.

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Section: Discussionmentioning

confidence: 99%

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

Lima

Hirata

2011

Information Sciences

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“…While the ontological representation of the aggregation structure of PIs, typical of the multidimensional model, is largely studied [24][25][26][27], the compound nature and consequent dependency among indicators is much less explored. In [28] formulas are specified through a proprietary script language aimed at the automatic generation of customised data marts, capable to calculate complex measures from data on the basis of their definitions.…”

Section: Data Warehousementioning

confidence: 99%

SemPI: A semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators

Diamantini

Potena

Storti

2016

Future Generation Computer Systems

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“…This is supported by some kind of semantic linkage between data cubes [39,16]. These inter-schema relationships are explicitly modelled in certain frameworks through the use of for example, flows or correlations [2].…”

Section: Drill Acrossmentioning

confidence: 99%

Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses

Pardillo

Mazón

Trujillo

2010

Information Sciences

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Data warehouse enhancement: A semantic cube model approach

Cited by 28 publications

References 32 publications

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

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

SemPI: A semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators

Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses

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