Integrated Decision-Making Based on OLAP

Improve the OLAP analysis process

To search for a suitable model combining all the advantages of OLAP analysis, multicriteria analysis, and fuzzy analysis, it is necessary to establish a representative process when structuring and solving some complex and critical decision-making support problems. 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. 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. 1, which can be analyzed using OLAP analysis operators and multicriteria analysis methods.

Classical cycle of the OLAP analysis

This analysis context is based on the design of a new multidimensional model of the data cube called multicriteria/logistic model (MLM), to understand and simplify the combination of the concepts of multicriteria analysis and OLAP analysis, as shown in Fig. 2.

Evolution of the analysis cycle

The second axis aims to realize the technical integration of multicriteria analysis, fuzzy analysis, and OLAP analysis in the data processing and analysis process. This integration is achieved by incorporating directly multicriteria analysis techniques, when exploring data, using MDX queries to interact in a multidimensional manner with the new proposed data cube model, and indirectly via an external process which is complementary to that of OLAP. Moreover, in order to avoid ambiguity and uncertainty of data, we propose to integrate fuzzy analysis in the analysis process. This improves and expands the technical and analytic capabilities of the decision support systems while creating an advanced analysis process that is adapted to the strategic decision-making needs.

Proposed data model

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, derived from a structured business datamart. It is surrounded by a unique circle of dimensions constituting the multidimensional and multicriteria characteristics specific to the decision makers' needs and to the mode of extraction and analysis of the data during the decision-making process as presented in Fig. 3.

Multidimensional modeling of the new OLAP cube model

The abstract representation of the new proposed data cube model is shown in Fig. 4 with respect to the multidimensional modeling of the data already presented in Fig. 3.

Proposed prototype: general architecture

The general architecture of the software prototype, allowing us to take into consideration the new data cube model presented previously (Figs. 3, 4), consists of two evaluation processes: The process of criteria evaluation (AMCD interface) or that of alternatives evaluation (OLAP_MML interface and Visual Promethee interface) as illustrated in Fig. 5. The proposed prototype represents a simplified implementation of our decision-making approach proposed in our previous contributions combining multicriteria analysis, fuzzy analysis, and OLAP systems. 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.

General architecture of the proposed software solution

At the operational level, the integration mode of these analysis processes is an incomplete mode (loose coupling), where the AMCD, OLAP_MML, and Visual PROMETHEE interfaces remain completely independent. The aim of the AMCD interface is to ensure collective decision making by computing the weights of the criteria selected from the MLM data cube (Figs. 4, 5) based on the geometric mean method algorithm. This evaluation is carried out via a group of three decision makers using a linguistic scale for evaluation. For the OLAP_MML interface, for which the objective is to be connected to the Mondrian OLAP server, it is used to identify potential actions from our MLM and to analyze them for a specific period of time. This analysis interface allows visualizing the result of the score of each action in the form of a final ranking. Finally, the criteria weights calculated via the AMCD interface and the alternatives analyzed via the OLAP_MML application are taken as input variables at the Visual Promethee program level, integrating the PROMETHEE multicriteria method. This latter program helps to simplify the final assessment of alternatives by allowing decision makers to intervene during the decision-making process.

Proposed processing and analysis algorithms

To illustrate the overall functioning of our proposed prototype, we adopt the following algorithms detailed in Table 1.