A Methodology and Tool for Rapid Prototyping of Data Warehouses Using Data Mining: Application to Birds Biodiversity

Sautot, Lucile; Bimonte, Sandro; Journaux, Ludovic; Faivre, Bruno

doi:10.1007/978-3-319-11587-0_23

Cited by 5 publications

(5 citation statements)

References 12 publications

(17 reference statements)

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“…Moreover, we are also working to integrate our approach in the rapid prototyping methodology proposed in (Sautot et al, 2014a), and extending to help decision-makers and DW experts choose the right DM algorithms and parameters of the refinement algorithm (source node, contextual dimensions, etc.). Future work concerns the usage of the formal evaluation framework Goal Question Metric (Briand et al, 2002) to evaluate our methodology.…”

Section: Discussionmentioning

confidence: 99%

“…(Ceci et al, 2011) use a hierarchical clustering to integrate continuous variables as dimensions in an OLAP schema. In the same line, (Sautot et al, 2014b) propose using Agglomerative Clustering for designing hierarchies, and the integration in a rapid prototyping methodology is presented in (Sautot et al, 2014a). However, all existing works define hierarchies using only either dimensional data (i.e.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Dimension Enrichment with Factual Data During the Design of Multidimensional Models: Application to Bird Biodiversity

Sautot

Bimonte²,

Journaux

et al. 2015

Enterprise Information Systems

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Data warehouses (DW) and OLAP systems are business intelligence technologies allowing the on-line analysis of huge volume of data according to users' needs. The success of DW projects essentially depends on the design phase where functional requirements meet data sources (mixed design methodology) (Phipps and Davis, 2002). However, when dealing with complex applications existing design methodologies seem inefficient since decision-makers define functional requirements that cannot be deduced from data sources (data driven approach) and/or they have not sufficient application domain knowledge (user driven approach) (Sautot et al., 2014b). Therefore, in this paper we propose a new mixed refinement design methodology where the classical data-driven approach is enhanced with data mining to create new dimensions hierarchies. A tool implementing our approach is also presented to validate our theoretical proposal.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dimension Enrichment with Factual Data During the Design of Multidimensional Models: Application to Bird Biodiversity

Sautot

Bimonte²,

Journaux

et al. 2015

Enterprise Information Systems

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“…The main idea is grouping similar facts originating from the source facts and only using data for the one representative fact in each cluster. In this way, the number of source facts decreases and, the number of context instances decreases too [12].…”

Section: Clustering Source Factsmentioning

confidence: 99%

A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into Dimensions

Sautot¹,

Bimonte²,

Journaux³

2021

IEEE Trans. Knowl. Data Eng.

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A decision support system is used by decision makers for a long time. But, in some cases, the originally designed multidimensional schema does not cover the entire needs of decision makers, which can change over time. One such unfulfilled needs, is using facts to describe dimension members. In this article, we propose a methodology to transform the constellation schema of a data warehouse by integrating factual data into a dimension. The proposed methodology and algorithms enrich a constellation multidimensional schema with new analytical possibilities for decision makers. This enrichment has repercussions for the entire multidimensional schema that are managed by multidimensional modeling, hierarchy calculation and the hierarchy version. In this article, we present a theoretical view of the proposed methodology supported by a case study, an implemented prototype and a complete evaluation based on a standard benchmark.

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“…), which are often unknown to ecologists, who tend to be unskilled IS and OLAP users. Ecologists may therefore find it very hard to express their analytical needs in terms of measures and dimensions on a conceptual schema, i.e., without visualizing sample query results (Sautot et al, 2014). These decisionmakers need DW prototypes to validate their analytical needs in terms of dimensions and measures.…”

Section: Rationalementioning

confidence: 99%

“…As yet, however, no rapid prototyping methodology has integrated DM into DW design. Therefore, in a preliminary study (Sautot et al, 2014), we briefly presented a new prototyping methodology for DWs, using clustering methods to define the DW schema. Building upon our previous study, we now include more advanced DM methods, thus proposing the following improvements:…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Model Design using Data Mining

Bimonte

Sautot

Journaux

et al. 2017

International Journal of Data Warehousing and Mining

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Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transform multidimensional schemata according to the results of the DM algorithms; (iv) a tool implementing the proposed methodology; (v) a full validation, based on a real case study concerning bird biodiversity. In conclusion, we confirm the rapidity and efficacy of our methodology and tool in providing a multidimensional schema to satisfy decision-maker analytical needs.

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A Methodology and Tool for Rapid Prototyping of Data Warehouses Using Data Mining: Application to Birds Biodiversity

Cited by 5 publications

References 12 publications

Dimension Enrichment with Factual Data During the Design of Multidimensional Models: Application to Bird Biodiversity

Dimension Enrichment with Factual Data During the Design of Multidimensional Models: Application to Bird Biodiversity

A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into Dimensions

Multidimensional Model Design using Data Mining

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