Reducing Multidimensional Data

Atigui, Faten; Ravat, Franck; Song, Jiefu; Zurfluh, Gilles

doi:10.1007/978-3-319-10160-6_19

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…At the analysis level, we intend to propose some advanced multi-state analysis operators by regrouping several elementary operators. At the implementation level, in our previous work (Atigui et al, 2014;Atigui et al, 2015), the reduced MDW has been implemented in an R-OLAP environment. Thus, one extension consists in evaluating the efficacy and efficiency of data reduction in different environments, such as M-OLAP and H-OLAP.…”

Section: Discussionmentioning

confidence: 99%

“…To evaluate the efficiency of multi-state OLAP analyses, we can study query execution efficiency in reduced and unreduced MDWs. In our previous work (Atigui, Ravat, Song, Teste, & Zurfluh, 2015;Atigui, Ravat, Song, & Zurfluh, 2014), we have compared some key indicators about query execution efficiency by applying different types of queries to different types of R-OLAP MDWs. Several experimental assessments are carried out in DBMS Oracle 11g with the following hardware configuration:…”

Section: Efficiency Of Multi-state Olap Analysesmentioning

confidence: 99%

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OLAP Analysis Operators for Multi-State Data Warehouses

Ravat

Song

Teste

2016

International Journal of Data Warehousing and Mining

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Data reduction in Multidimensional Data Warehouses (MDWs) allows increasing the efficiency of analysis and facilitating decision-makers' tasks. In this paper, the authors model a MDW containing reduced data through a set of states. Each state is valid for a certain period of time; it contains only useful information according to decision-makers' needs. In order to carry out analyses in a MDW composed of multiple states, an extension of traditional OLAP analysis operators is required. In this paper, the authors define a set of OLAP operators compatible with reduced MDWs. For each operator, they propose a user-oriented definition along with an algorithmic translation. To show the feasibility and the efficiency of the proposed concepts, they implement the analysis operators in an R-OLAP framework.

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Section: Discussionmentioning

confidence: 99%

Section: Efficiency Of Multi-state Olap Analysesmentioning

confidence: 99%

OLAP Analysis Operators for Multi-State Data Warehouses

Ravat

Song

Teste

2016

International Journal of Data Warehousing and Mining

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“…Finally, with regard to integration of different data warehouses, to the best of our knowledge only Abello (2006) and Atigui (2014) address this topic. (Abello, 2006) proposes a conceptual model based on UML to represent constellation schema (i.e., several facts with a shared dimension) where facts can be associated using the aggregation UML relationship, but they do not give details about how this integration is done.…”

Section: Related Workmentioning

confidence: 99%

“…(Abello, 2006) proposes a conceptual model based on UML to represent constellation schema (i.e., several facts with a shared dimension) where facts can be associated using the aggregation UML relationship, but they do not give details about how this integration is done. (Atigui, 2014) provides a set of formal operators to reduce cubes, to feed a classical data warehouse using data coming from other warehouses. This proposal does not support temporal aspects.…”

Section: Related Workmentioning

confidence: 99%

Business Intelligence Indicators

Boussaïd

Bimonte

Schneider

2016

International Journal of Data Warehousing and Mining

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Nowadays, more and more data are available for decisional analysis and decision-making based on different indicators. Although different decision-making technologies have been developed, the authors note the lack of a conceptual framework for the definition and implementation of these indicators. In this paper, they propose a first classification of these indicators. Furthermore, motivated by the need for formalism for the definition of these indicators at a conceptual level, they present the Business Intelligence Indicators (BI2) UML profile to represent indicators for OLAP, OLTP and streaming technologies. They also present their implementation in existing industrial tools. In addition, they show how these indicators can coexist in the same environment to exchange data through a chaining model and its implementation.

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Facilitate Effective Decision-Making by Warehousing Reduced Data

Atigui

Ravat

Song

et al. 2015

International Journal of Decision Support System Technology

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The authors' aim is to provide a solution for multidimensional data warehouse's reduction based on analysts' needs which will specify aggregated schema applicable over a period of time as well as retain only useful data for decision support. Firstly, they describe a conceptual modeling for multidimensional data warehouse. A multidimensional data warehouse's schema is composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. The derivation between states is carried out through combination of reduction operators. Secondly, they present a meta-model which allows managing different states of multidimensional data warehouse. The definition of reduced and unreduced multidimensional data warehouse schema can be carried out by instantiating the meta-model. Finally, they describe their experimental assessments and discuss their results. Evaluating their solution implies executing different queries in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema and reduced snowflake schema. The authors show that queries are more efficiently calculated within a reduced star schema.

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Reducing Multidimensional Data

Cited by 3 publications

References 10 publications

OLAP Analysis Operators for Multi-State Data Warehouses

OLAP Analysis Operators for Multi-State Data Warehouses

Business Intelligence Indicators

Facilitate Effective Decision-Making by Warehousing Reduced Data

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