Processing And Managing Complex Data for Decision Support

To get Benson' Microbiological Applications: Laboratory Manual in General Microbiology, Short Version [Spiral-bound]. Benson' Microbiological Applications: .

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Indeed, the main contribution of this paper is to propose a decision-making solution combining multicriteria analysis, fuzzy analysis, and OLAP systems, in order to generate an advanced analysis process adapted to the needs of decision makers.

Using Analytical Data to Support Decision-Making | Moody's Analytics

The development of this solution is performed according to two main axes. The first axis aims to provide an analytic context, which is different from the classical analysis cycle presented in Fig. Open image in new window. The proposed data model, which is based on the multidimensional modeling of the data warehouse, is a star dimensional structure that provides a fact table representing the new OLAP cube.

This fact table contains observable, measurable, and numerical data Kimball and Ross , derived from a structured business datamart. The abstract representation of the new proposed data cube model is shown in Fig. The general architecture of the software prototype, allowing us to take into consideration the new data cube model presented previously Figs. The proposed prototype represents a simplified implementation of our decision-making approach proposed in our previous contributions Boutkhoum et al.

It is divided into two layers: first, the Data warehouse containing the datamart that feeds the proposed data cube model; and second the interrogation and presentation layer that consists of the Mondrian OLAP server, allowing the exploration and interrogation of cube data via MDX queries. These queries are sent from the user interface to view and visualize the different analysis results. Description of the case study Issues related to the Green Supply Chain Management at the level of mining activities have serious social—environmental consequences and underlying economic implications Hilson and Nayee ; Poulton et al.

In this illustrative example linked to the field of green logistics, potential itineraries for the transport of chemicals in Casablanca industrial region see Fig. It examines several itineraries and controls their evolution during a period of time beginning from to , according to several evaluation criteria as shown in Fig.

The criteria presented in Fig. This segmentation aims to simplify the selection of these criteria and consider only those with a high final score. In this context, the evaluation of these criteria via the AMCD interface is carried out according to six complementary steps as illustrated in Fig. The first step consists in specifying the number of criteria to be considered in the evaluation. This is used by the AMCD interface to generate the comparison matrix corresponding to the number of criteria already specified.

At the beginning of the evaluation, according to the number of criteria set by decision makers using the AMCD interface step 1 presented in Fig. The role of decision makers is to complete the criteria evaluation matrix by their assessments for each criterion in relation to the others Fig. After finalizing the evaluation matrix by the linguistic appreciations of the decision group, we transform this qualitative data into numerical ones via fuzzy triangular numbers, in order to aggregate them as shown in Fig. Then, based on the geometric mean already obtained, we calculate the fuzzy weights of the criteria which are in the form of a triangular fuzzy number using Eqs.

Quality Decisions

The final step of MCA process is to make the defuzzification of the fuzzy weight obtained in Fig. The final standardized weight resulting from this defuzzification is then obtained via Eq. Consequently, we execute MDX queries via the Mondrian OLAP server Pentaho in order to illustrate the final results of the evaluation of all itineraries selected from the data cube as presented in Fig. These alternatives result from the aggregation of the values of the criteria versus the alternatives during the period — Linguistic variables Fuzzy triangular scale Good G 0.

MP 0, 0, 0. In the same context, the V-shape preference function see Fig. MP Itinerary-Ref L. The geometrical analysis for interactive aid GAIA integrated in Visual Promethee program is used as a visualization method complementing the PROMETHEE ranking methodology, which will help to display graphically the relative position of itineraries in terms of contributions to the selected criteria Fig. To sum up, the positive flow, negative flow and net outranking flow allowing to provide the final ranking of itineraries are obtained as shown in Fig.

The results obtained at this level may be sufficient, taking into consideration the objective of the decision. Acknowledgements The authors wish to thank Mr. Competing interests The authors declare that they have no competing interests. Funding There was no funding for this study. Decis Support Syst — Arab J Sci Eng 40 8 — Boutkhoum O, Hanine M, Boukhriss H, Agouti T, Tikniouine A a Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices.

Buckley JJ Fuzzy hierarchical analysis. J Parallel Distrib Comput.

Decision-Making and Problem-Solving

Environ Syst Decis. Inf Syst e-Bus Manag.

Hilson G, Nayee V Environmental management system implementation in the mining industry: a key to achieving cleaner production. Comput Sci Appl — Google Scholar.

1.1 A brief history of Decision Support Systems

Kaya M, Alhajj R Development of multidimensional academic information networks with a novel data cube based modeling method. Kimball R The data warehouse toolkit: practical techniques for building dimensional data warehouses. Wiley, New York.


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Kimball R, Ross M The data warehouse toolkit: the complete guide to dimensional modeling, 2nd edn. Wiley, Hoboken Google Scholar. Scientometrics 2 — CrossRef Google Scholar.

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